Set global variables

From TO/JS - This section sets some flags that can be tweaked to re-run the analysis with different filtering parameters:

RUN_EXAMPLE: allows discarding alleles that have a frequency below a threshold (defaults to FALSE), WITHIN_INDIVIDUAL_ALLELE_FREQ_THR: allows discarding alleles that have a frequency below a threshold (defaults to 0), BENCHMARK_MARKERS: list of markers to include in the analysis (defaults to the full list established by Jason, could be reduced down to 3 for memory optimization), MAX_MOI_TO_INCLUDE: allows discarding participant data when they exhibit very complex infection patterns, counted as the total number of clones across all infection events (defaults to 8), PRIOR_3RS: a named vector with prior probabilities of recrudescence (C), relapse (L) or reinfection (I) (defaults to uniform, i.e. 1/3 each, but tweaked to minimize recrudescence in the present study).

## RUN_EXAMPLE
##   Boolean flag to enable/disable running the example Pv3Rs posterior 
##   computation on a single episode sampled at random from the dataset.
## DEFAULT: FALSE
RUN_EXAMPLE <- TRUE

## WITHIN_INDIVIDUAL_ALLELE_FREQ_THR
##   Minimum threshold above which an allele is preserved in individual
##   haplotype data to be preserved when reconstructing infection 
##   history. Note that this will *not* discard these alleles from the
##   population-level allele frequency that is derived from the initial
##   visit, but only discard that allele from individual observations 
##   when it is 'too rare to be exploited'.
## DEFAULT: 0
WITHIN_INDIVIDUAL_ALLELE_FREQ_THR <- 0

## BENCHMARK_MARKERS
##   Vector of character string with names matching those from the 
##   markers of interest for amplicon sequencing. Only the markers 
##   listed in that vector will be preserved in individual-level data
##   and identity of infection will be solely based on the alleles 
##   observed for these markers.
##   The markers are listed by importance as discovered by Jason.
## DEFAULT: all markers (could be suboptimal/too memory-consuming?)
BENCHMARK_MARKERS <- c(
  "Chr05",
  "Chr07", 
  "Chr09", 
  "Chr10",
  "Chr08",
  "Chr13",
  "Chr11",
  "Chr03",
  "Chr01",
  "Chr02",
  "Chr14"
)

## MAX_MOI_TO_INCLUDE
##   As per Aimee's guidelines: 
##     We do not recommend running compute_posterior() for data 
##     whose total genotype count exceeds eight, where the total 
##     genotype count is the sum of per-episode maximum per-marker 
##     allele counts.
##   The MAX_MOI_TO_INCLUDE expects an integer that will discard all
##   individuals having a summed MOI > 8 across all recorded episodes.
## DEFAULT: 8
MAX_MOI_TO_INCLUDE <- 8

## PRIORS_3RS
##   A vector of probabilities, summing to 1, corresponding to 
##   the probability of each stage for the 3Rs for Pv episodes.
##   The vector order is re(C)rudescence, re(L)apse, re(I)nfection.
##   In this clinical trial, we assume recrudescence is possible so, 
##   we use the default priors 
## DEFAULT: c("C" = 1/3, "L" = 1/3, "I" = 1/3)
PRIOR_3RS <- c("C" = 1/3, "L" = 1/3, "I" = 1/3)
# Note: the below probabilities were used for SeroTAT study because it does not involve treatment at baseline
# PRIOR_3RS <- c("C" = 0.10, "L" = 0.45, "I" = 0.45)

Solomon Islands data curation

Read in data from Solomon Islands:

sols <- read.csv(here("data/final", "sols_raw_data.csv"))

# head(sols)
# names(sols)

Number of unique haplotypes (ie marker cardinality):

sols %>% 
  group_by(marker_id) %>% 
  summarise(unique_haplotypes = n_distinct(Haplotype))
## # A tibble: 11 × 2
##    marker_id unique_haplotypes
##    <chr>                 <int>
##  1 Chr01                     4
##  2 Chr02                    10
##  3 Chr03                     6
##  4 Chr05                    13
##  5 Chr07                     9
##  6 Chr08                     2
##  7 Chr09                     7
##  8 Chr10                     6
##  9 Chr11                     3
## 10 Chr13                     5
## 11 Chr14                     6

Counts of each haplotype:

sols %>% count(marker_id, Haplotype)
##    marker_id Haplotype   n
## 1      Chr01   Chr01-1  76
## 2      Chr01   Chr01-2  48
## 3      Chr01   Chr01-3  18
## 4      Chr01   Chr01-4   5
## 5      Chr02   Chr02-1  27
## 6      Chr02  Chr02-11   3
## 7      Chr02   Chr02-2  25
## 8      Chr02   Chr02-3  28
## 9      Chr02   Chr02-4  29
## 10     Chr02   Chr02-5  25
## 11     Chr02   Chr02-6  13
## 12     Chr02   Chr02-7  10
## 13     Chr02   Chr02-8   6
## 14     Chr02   Chr02-9   4
## 15     Chr03   Chr03-1  84
## 16     Chr03   Chr03-2  36
## 17     Chr03   Chr03-3   9
## 18     Chr03   Chr03-4  18
## 19     Chr03   Chr03-5   8
## 20     Chr03   Chr03-6   2
## 21     Chr05   Chr05-1  26
## 22     Chr05  Chr05-10   2
## 23     Chr05  Chr05-11   4
## 24     Chr05  Chr05-12   2
## 25     Chr05  Chr05-13   3
## 26     Chr05   Chr05-2   8
## 27     Chr05   Chr05-3  17
## 28     Chr05   Chr05-4  16
## 29     Chr05   Chr05-5  16
## 30     Chr05   Chr05-6  13
## 31     Chr05   Chr05-7  13
## 32     Chr05   Chr05-8  13
## 33     Chr05   Chr05-9   5
## 34     Chr07   Chr07-1  57
## 35     Chr07  Chr07-10   3
## 36     Chr07  Chr07-11   2
## 37     Chr07   Chr07-2  39
## 38     Chr07   Chr07-3  33
## 39     Chr07   Chr07-4  18
## 40     Chr07   Chr07-5  11
## 41     Chr07   Chr07-6   8
## 42     Chr07   Chr07-9   3
## 43     Chr08   Chr08-1 108
## 44     Chr08   Chr08-2  23
## 45     Chr09   Chr09-1  80
## 46     Chr09   Chr09-2  22
## 47     Chr09   Chr09-3  34
## 48     Chr09   Chr09-4  18
## 49     Chr09   Chr09-5   9
## 50     Chr09   Chr09-7   2
## 51     Chr09   Chr09-9   2
## 52     Chr10   Chr10-1  66
## 53     Chr10   Chr10-2  52
## 54     Chr10   Chr10-3  16
## 55     Chr10   Chr10-4  11
## 56     Chr10   Chr10-5   6
## 57     Chr10   Chr10-6   5
## 58     Chr11   Chr11-1  78
## 59     Chr11   Chr11-2  39
## 60     Chr11   Chr11-3  33
## 61     Chr13   Chr13-1  40
## 62     Chr13   Chr13-2  55
## 63     Chr13   Chr13-3  45
## 64     Chr13   Chr13-4   5
## 65     Chr13   Chr13-5   4
## 66     Chr14   Chr14-1  51
## 67     Chr14   Chr14-2  46
## 68     Chr14   Chr14-3  25
## 69     Chr14   Chr14-4  21
## 70     Chr14   Chr14-5  16
## 71     Chr14   Chr14-6   9

“MOI”:

sols %>% 
  group_by(sample, marker_id) %>%
  summarise(n_haplotypes = n(), .groups = 'drop') %>%
  group_by(sample) %>% 
  summarise(max_moi = max(n_haplotypes)) 
## # A tibble: 135 × 2
##    sample     max_moi
##    <chr>        <int>
##  1 AR-002-0         1
##  2 AR-006-0         2
##  3 AR-007-0         1
##  4 AR-008-0         2
##  5 AR-009-0         2
##  6 AR-015-0         2
##  7 AR-023-0         2
##  8 AR-024-0         1
##  9 AR-024-112       2
## 10 AR-024-56        2
## # ℹ 125 more rows

Recurrences

Participants with more than one episode for inference (n=41 participants, n=99 Pv isolates):

sols %>% 
  select(info, episodes) %>% 
  distinct() %>% 
  arrange(info) %>% 
  filter(episodes>1) 
##      info episodes
## 1  AR-024        5
## 2  AR-067        2
## 3  AR-081        3
## 4  AR-098        2
## 5  AR-110        2
## 6  AR-133        3
## 7  AR-140        3
## 8  AR-142        4
## 9  AR-160        2
## 10 AR-173        2
## 11 AR-176        3
## 12 AR-187        4
## 13 AR-192        2
## 14 AR-214        2
## 15 AR-221        2
## 16 AR-234        2
## 17 AR-237        2
## 18 AR-246        2
## 19 AR-248        2
## 20 AR-250        2
## 21 AR-258        2
## 22 AR-274        2
## 23 AR-288        2
## 24 AR-300        2
## 25 AR-302        2
## 26 AR-304        2
## 27 AR-306        3
## 28 AR-322        2
## 29 AR-325        2
## 30 AR-326        2
## 31 AR-328        3
## 32 AR-341        2
## 33 AR-343        2
## 34 AR-345        2
## 35 AR-346        2
## 36 AR-348        2
## 37 AR-355        2
## 38 AR-359        6
## 39 AR-366        2
## 40 AR-382        2
## 41 AR-383        2
ids_recurrent <- sols %>% clean_names() %>% filter(episodes > 1) %>% distinct(info)
sols_recurrent <- sols %>% clean_names() %>%filter(episodes > 1)

# setdiff(ids_recurrent$info, sols_recurrent$info)
# setdiff(sols_recurrent$info, ids_recurrent$info)

# sols_recurrent %>% distinct(sample) %>% arrange(sample) # 99 isolates
sols_recurrent %>% 
  distinct(sample) %>% 
  separate(sample, into = c("sample", "episode_day"), sep = "-(?=[0-9]+$)") %>% 
  ggplot(aes(x = episode_day, y = sample)) +
    geom_line(aes(group = sample), color = "darkgrey") +
    geom_point() +
    theme_bw()

Data on episode number and time since last episode

episode_summary <- sols_recurrent %>% 
  distinct(info, info_d, vis_date, sample) %>% 
  mutate(vis_date = mdy(vis_date)) %>%
  arrange(info, vis_date) %>% 
  group_by(info) %>% 
  mutate(
    # get the episode number
    episode_number = row_number(),
    # calculate days since enrolment, just a check with dates
    days_since_enrolment = as.integer(vis_date - min(vis_date[info_d == 0])),
    # calculate days since last episode using lag() and making enrolment episodes 0 days since last
    days_since_last_episode = replace_na(as.integer(vis_date - lag(vis_date)), 0)
  ) %>% 
  ungroup()

Haplotype frequencies

We want to remove any haplotypes that appear only once at very low frequencies. Haplotypes should already be filtered to be observed in at least 2 samples and within-host frequency >=1% (in full dataset).

singleton_haps <- sols_recurrent %>% 
  select(sample, marker_id, haplotype, frequency, count) %>% 
  count(haplotype) %>% 
  arrange(n) %>% 
  filter(n==1) %>% 
  pull(haplotype)

sols_recurrent %>% 
  filter(haplotype %in% singleton_haps) %>% 
  ggplot(aes(x = haplotype, y = count)) +
    geom_hline(yintercept = 100, linetype = "dashed") +
    geom_point(aes(color = frequency, shape = follow_x), size = 3) +
    labs(x = "singleton haplotype",
         y = "read count",
         color = "within-sample frequency",
         shape = "timepoint") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

## Haplotypes for inference As per Aimee, to avoid bias due to within-host selection of recrudescent parasites, we use only enrolment episodes to estimate population-level alelle frequencies. So, we will filter out any haplotypes that do not appear at baseline (drop n=8 haplotypes). Details below on their within-sample frequencies and read counts.

# n=569 haps
baseline_haps <- sols_recurrent %>% 
  filter(follow_x == "baseline") %>% 
  pull(haplotype)

sols_recurrent %>% 
  filter(!haplotype %in% baseline_haps) %>% 
  select(sample, marker_id, haplotype, frequency, count) %>% 
  arrange(frequency, count)
##       sample marker_id haplotype frequency count
## 1 AR-081-168     Chr09   Chr09-7    0.1066   716
## 2 AR-343-112     Chr07  Chr07-11    0.2332  2302
## 3 AR-081-140     Chr09   Chr09-7    0.7282  3518
## 4  AR-024-70     Chr05  Chr05-13    0.9918  7028
## 5  AR-024-56     Chr05  Chr05-13    0.9933  4720
## 6 AR-081-168     Chr05  Chr05-10    1.0000  9622
## 7  AR-024-84     Chr05  Chr05-13    1.0000  9693
## 8 AR-081-140     Chr05  Chr05-10    1.0000  9745

Final data for inference

analysis_data <- sols_recurrent %>%
  select(subject_id = info, 
         sample_id = sample, 
         treatment_arm = trt_label, 
         days_since_treatment = info_d,
         timepoint = follow_x,
         visit_date = vis_date,
         age_years = age_y, 
         sex = gender, 
         episodes, 
         marker_id, 
         haplotype, 
         type,
         frequency,
         mean_moi,
         max_moi) %>% 
  # add extra epi info on episode number and time since last episode
  left_join(episode_summary %>% select(subject_id = info, 
                                       sample_id = sample, 
                                       episode_number,
                                       days_since_enrolment,
                                       days_since_last_episode),
            by = c("subject_id", "sample_id"))  %>% 
  # keep only haplotypes present at baseline
  filter(haplotype %in% baseline_haps) %>% 
  # ensure dates are date class
  mutate(visit_date = mdy(visit_date)) 

Baseline allele frequencies

test_df <- analysis_data %>% 
  # Use only baseline samples
  filter(timepoint == "baseline") %>% 
  # filter data frame by marker
  filter(marker_id == "Chr11")

test_df %>% 
      mutate(haplotype = factor(haplotype)) %>% 
      select(sample_id, haplotype, frequency) %>% 
      pivot_wider(names_from = haplotype, 
                  values_from = frequency, 
                  values_fill = 0) %>% 
      pivot_longer(cols = -sample_id, 
                   names_to = "haplotype", 
                   values_to = "frequency") %>% 
      # Get population-level haplotype frequency, 
      # correcting for when within-individual sum is not equal 
      # to 1, as can happen when a minority clone is <2% 
      group_by(sample_id) %>% 
      mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>% 
  
      # get population-level mean freq
      group_by(haplotype) %>% 
      summarise(poplevel_mean_freq = mean(frequency, na.rm = TRUE)) %>% 
      adorn_totals("row")
## Derive baseline allele frequency - This is modified from Thomas/Jason script for our data
baseline_fs <- analysis_data %>% 
  
  # Use only baseline samples
  filter(timepoint == "baseline") %>% 
  
  # split data frame by marker
  group_by(marker_id) %>% 
  group_split() %>% 
  
  # Derive a within-marker list of frequencies, by individual
  lapply(function(x) {
    x <- x %>% 
      # Build a within-individual frequency table that 
      # always includes every haplotype (even the ones absent)
      mutate(haplotype = factor(haplotype)) %>% 
      select(sample_id, haplotype, frequency) %>% 
      pivot_wider(names_from = haplotype, 
                  values_from = frequency, 
                  values_fill = 0) %>% 
      pivot_longer(cols = -sample_id, 
                   names_to = "haplotype", 
                   values_to = "frequency") %>% 
      # Get population-level haplotype frequency, 
      # correcting for when within-individual sum is not equal 
      # to 1, as can happen when a minority clone is <2%
      group_by(sample_id) %>% 
      mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>% 
      group_by(haplotype) %>% 
      summarise(frequency_pop_mean = mean(frequency, na.rm = TRUE))
    
    return(deframe(x))
  }) %>% 
  # get marker_id from group_keys from the group dfs 
  setNames(nm = analysis_data %>% group_by(marker_id) %>% group_keys() %>% pull(marker_id))

# Here we can also save as dataframe for easier printing and table-ready for paper
baseline_fs_df <- analysis_data %>% 
  # Use only baseline samples
  filter(timepoint == "baseline") %>% 
  
  # Group by marker_id and sample_id for further calculations
  group_by(marker_id, sample_id) %>% 
  
  # Build a within-individual frequency table that always includes every haplotype (even the ones absent)
  mutate(haplotype = factor(haplotype)) %>% 
  select(marker_id, sample_id, haplotype, frequency) %>% 
  pivot_wider(names_from = haplotype, values_from = frequency, values_fill = list(frequency = 0)) %>% 
  pivot_longer(cols = -c(marker_id, sample_id), names_to = "haplotype", values_to = "frequency") %>% 
  
  # Get population-level haplotype frequency, correcting for when within-individual sum is not equal to 1
  group_by(marker_id, sample_id) %>% 
  mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>% 
  group_by(marker_id, haplotype) %>% 
  summarise(frequency_pop_mean = mean(frequency, na.rm = TRUE), .groups = 'drop') %>% 
  
  # Ensure haplotypes are correctly associated with their marker_id - note that this works for us because , in future would have to make this flexible to allow for haplotype names that are not reliant on having marker_id
  filter(str_detect(haplotype, marker_id))

baseline_fs_df
## # A tibble: 67 × 3
##    marker_id haplotype frequency_pop_mean
##    <chr>     <chr>                  <dbl>
##  1 Chr01     Chr01-1              0.534  
##  2 Chr01     Chr01-2              0.330  
##  3 Chr01     Chr01-3              0.112  
##  4 Chr01     Chr01-4              0.0244 
##  5 Chr02     Chr02-1              0.228  
##  6 Chr02     Chr02-11             0.00467
##  7 Chr02     Chr02-2              0.133  
##  8 Chr02     Chr02-3              0.198  
##  9 Chr02     Chr02-4              0.191  
## 10 Chr02     Chr02-5              0.101  
## # ℹ 57 more rows

Pv3Rs

Example on one participant: AR-067

# This has been modified from Thomas/Jason script 

## Pick an individual at random to run Pv3Rs
# indiv_name <- sample(unique(analysis_data$subject_id), size = 1)
indiv_name <- "AR-067"

## Prepare the data
# 1- Subset haplotype data to specific individual and apply filters
indiv_haplotype_data <- analysis_data %>% 
  # Restrict to a single patient
  filter(subject_id == indiv_name) %>% 
  # Restrict to a subset of markers, if needed
  filter(marker_id %in% BENCHMARK_MARKERS) %>% 
  # Restrict to summed MOI below threshold
  group_by(subject_id, episode_number, marker_id) %>% 
  mutate(MOI_per_marker = sum(n())) %>% 
  group_by(subject_id, episode_number) %>% 
  mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))

# 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
indiv_MOI <- indiv_haplotype_data %>% 
  select(subject_id, episode_number, 
         marker_id, starts_with("MOI_")) %>% 
  distinct() %>% 
  group_by(subject_id, episode_number) %>% 
  # Get highest per-marker MOI only for each episode
  # (drop marker_id in case of ties with highest per-marker MOI)
  select(-marker_id) %>% 
  distinct() %>% 
  filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))

Prepare test data

# Finish data preparation
  indiv_haplotype_data <- indiv_haplotype_data %>% 
    group_by(episode_number) %>% 
    group_split() %>% 
    lapply(function(x) {
      res <- x %>% 
        select(sample_id, episode_number, 
               marker_id, haplotype, frequency) %>% 
        # For sensitivity analysis, allow to include/drop allele 
        # based on their within-individual frequency
        filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>% 
        select(-sample_id, -episode_number, -frequency) %>% 
        distinct() %>% 
        # Prevent dropping of markers that are not characterised 
        # by setting .drop to FALSE
        group_by(marker_id, .drop = FALSE) %>% 
        group_split() %>% 
        lapply(function(y) {
          unique(y$haplotype)
        })
      
      # Returned a list named with each episode, 
      # setting marker allele to NA in case none are observed
      return(lapply(setNames(res, BENCHMARK_MARKERS), 
                    function(y) {
                      if (length(y) == 0) return(NA) else return(y)
                    }))
    })
# Run Aimee's posterior estimation
indiv_posterior <- Pv3Rs::compute_posterior(y  = indiv_haplotype_data, 
                                            fs = baseline_fs[BENCHMARK_MARKERS])
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
indiv_posterior
## $marg
##      C         L          I
## [1,] 0 0.9971211 0.00287892
## 
## $joint
##          C          L          I 
## 0.00000000 0.99712108 0.00287892

The joint posterior probability of relapse is 99.7%, when we look at the genetic data, this is in line with the inferred probability of relpase.

sols_recurrent %>% 
  filter(info == "AR-067") %>% 
  ggplot(aes(x = haplotype, y = factor(info_d), fill = factor(haplotype))) +
  geom_tile() +
    facet_grid(~marker_id, scales = "free_x") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

### Run Pv3Rs one random individual

# This has been modified from Thomas/Jason script 

## Pick an individual at random to run Pv3Rs
indiv_name <- sample(unique(analysis_data$subject_id), size = 1)

## Prepare the data
# 1- Subset haplotype data to specific individual and apply filters
indiv_haplotype_data <- analysis_data %>% 
  # Restrict to a single patient
  filter(subject_id == indiv_name) %>% 
  # Restrict to a subset of markers, if needed
  filter(marker_id %in% BENCHMARK_MARKERS) %>% 
  # Restrict to summed MOI below threshold
  group_by(subject_id, episode_number, marker_id) %>% 
  mutate(MOI_per_marker = sum(n())) %>% 
  group_by(subject_id, episode_number) %>% 
  mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))

# 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
indiv_MOI <- indiv_haplotype_data %>% 
  select(subject_id, episode_number, 
         marker_id, starts_with("MOI_")) %>% 
  distinct() %>% 
  group_by(subject_id, episode_number) %>% 
  # Get highest per-marker MOI only for each episode
  # (drop marker_id in case of ties with highest per-marker MOI)
  select(-marker_id) %>% 
  distinct() %>% 
  filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))

Now that the data has been subset, Pv3Rs::compute_posterior() will be called (only if the participant meets eligibility criteria as defined by the global variables set earlier in this report).

if (RUN_EXAMPLE & unique(indiv_MOI$MOI_summed) <= MAX_MOI_TO_INCLUDE) {
  # Verbose
  cat("Running Pv3Rs for: ", 
      indiv_name, 
      " (summed MOI = ", 
      unique(indiv_MOI$MOI_summed), 
      ").\n", 
      sep = "")
  
  # Finish data preparation
  indiv_haplotype_data <- indiv_haplotype_data %>% 
    group_by(episode_number) %>% 
    group_split() %>% 
    lapply(function(x) {
      res <- x %>% 
        select(sample_id, episode_number, 
               marker_id, haplotype, frequency) %>% 
        # For sensitivity analysis, allow to include/drop allele 
        # based on their within-individual frequency
        filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>% 
        select(-sample_id, -episode_number, -frequency) %>% 
        distinct() %>% 
        # Prevent dropping of markers that are not characterised 
        # by setting .drop to FALSE
        group_by(marker_id, .drop = FALSE) %>% 
        group_split() %>% 
        lapply(function(y) {
          unique(y$haplotype)
        })
      
      # Returned a list named with each episode, 
      # setting marker allele to NA in case none are observed
      return(lapply(setNames(res, BENCHMARK_MARKERS), 
                    function(y) {
                      if (length(y) == 0) return(NA) else return(y)
                    }))
    })
  
  # Run Aimee's posterior estimation
  indiv_posterior <- Pv3Rs::compute_posterior(y  = indiv_haplotype_data, 
                                              fs = baseline_fs[BENCHMARK_MARKERS])
} else {
  # Verbose
  cat("NOT Running Pv3Rs for: ", 
      indiv_name, 
      " because either summed MOI exceeds threshold (observed = ", 
      unique(indiv_MOI$MOI_summed), 
      ", MAX_MOI_TO_INCLUDE = ", 
      MAX_MOI_TO_INCLUDE, 
      "), or RUN_EXAMPLE was set to FALSE.\n", 
      sep = "")
  
  # Return NULL
  indiv_posterior <- NULL
}

indiv_posterior
sols_recurrent %>% 
  filter(info == indiv_name) %>% 
  ggplot(aes(x = haplotype, y = factor(info_d), fill = factor(haplotype))) +
  geom_tile() +
    facet_grid(~marker_id, scales = "free_x") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Run for all participants

# Start timer
t_start <- Sys.time()

# Initialize an empty list to store the results
indiv_posteriors <- list()

# Loop through each unique individual name
for (indiv_name in unique(analysis_data$subject_id)) {
  # Verbose
  cat("ID : ", indiv_name, "...\n", sep = "")
  
  ## Prepare the data
  # 1- Subset haplotype data to specific individual and apply filters
  indiv_haplotype_data <- analysis_data %>% 
    # Restrict to a single patient
    filter(subject_id == indiv_name) %>% 
    # Ensure episodes are in order
    arrange(episode_number) %>%  
    # Restrict to a subset of markers
    filter(marker_id %in% BENCHMARK_MARKERS) %>% 
    # Restrict to summed MOI below threshold
    group_by(subject_id, episode_number, marker_id) %>% 
    mutate(MOI_per_marker = sum(n())) %>% 
    group_by(subject_id, episode_number) %>% 
    mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>% 
    ungroup() %>% 
    mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))
  
  # 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
  indiv_MOI <- indiv_haplotype_data %>% 
    select(subject_id, episode_number, 
           marker_id, starts_with("MOI_")) %>% 
    distinct() %>% 
    group_by(subject_id, episode_number) %>% 
    # Get highest per-marker MOI only for each episode
    # (drop marker_id in case of ties with highest per-marker MOI)
    select(-marker_id) %>% 
    distinct() %>% 
    filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>% 
    ungroup() %>% 
    mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))
  
  ## Run Pv3Rs only if MOI below threshold         
  if (unique(indiv_MOI$MOI_summed) <= MAX_MOI_TO_INCLUDE) {
    # Verbose
    cat("Running Pv3Rs for: ", 
        indiv_name, 
        " (summed MOI = ", 
        unique(indiv_MOI$MOI_summed), 
        ").\n", 
        sep = "")
    
    # Preserve episode numbers for naming the output list
    indiv_episode <- unique(indiv_haplotype_data$episode_number)
    cat("The episode number is: ", indiv_episode) # printing to check episode order number is correct
    
    # Finish data preparation
    indiv_haplotype_data <- indiv_haplotype_data %>% 
      group_by(episode_number) %>% 
      group_split() %>% 
      lapply(function(x) {
        res <- x %>% 
          select(sample_id, episode_number, 
                 marker_id, haplotype, frequency) %>% 
          # For sensitivity analysis, allow to include/drop allele 
          # based on their within-individual frequency
          filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>% 
          select(-sample_id, -episode_number, -frequency) %>% 
          distinct() %>% 
          # Prevent dropping of markers that are not characterized 
          # by setting .drop to FALSE
          group_by(marker_id, .drop = FALSE) %>% 
          group_split() %>% 
          lapply(function(y) {
            unique(y$haplotype)
          })
        
        # Returned a list named with each episode, 
        # setting marker allele to NA in case none are observed
        return(lapply(setNames(res, BENCHMARK_MARKERS), 
                      function(y) {
                        if (length(y) == 0) return(NA) else return(y)
                      }))
      })
    
    # Run Aimee's posterior estimation
    indiv_posterior <- compute_posterior(y     = indiv_haplotype_data,
                                         fs    = baseline_fs[BENCHMARK_MARKERS], 
                                         prior = matrix(PRIOR_3RS, 
                                                        nrow     = length(indiv_haplotype_data), 
                                                        ncol     = length(PRIOR_3RS), 
                                                        byrow    = TRUE, 
                                                        dimnames = list(c(1:length(indiv_haplotype_data)), 
                                                                        names(PRIOR_3RS))))
    
  } else {
    # Verbose
    cat("NOT Running Pv3Rs for: ", 
        indiv_name, 
        " because summed MOI exceeds threshold (observed = ", 
        unique(indiv_MOI$MOI_summed), 
        ", MAX_MOI_TO_INCLUDE = ", 
        MAX_MOI_TO_INCLUDE, 
        ").\n", 
        sep = "")
    
    # Return NULL
    indiv_episode    <- unique(indiv_haplotype_data$episode_number)
    indiv_posterior <- NULL
  }
  
  # Append the results to the list
  indiv_posteriors[[indiv_name]] <- list("subject_id" = indiv_name, 
                                         "episode_number"       = indiv_episode, 
                                         "Pv3Rs"       = indiv_posterior)
}
## ID : AR-024...
## Running Pv3Rs for: AR-024 (summed MOI = 7).
## The episode number is:  1 2 3 4 5
## Number of valid relationship graphs (RGs) is 14718
## =============================================================================|
## Computing log p(Y|RG) for 14718 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-221...
## Running Pv3Rs for: AR-221 (summed MOI = 3).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-343...
## Running Pv3Rs for: AR-343 (summed MOI = 4).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-366...
## Running Pv3Rs for: AR-366 (summed MOI = 4).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 30
## =============================================================================|
## Computing log p(Y|RG) for 30 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-187...
## Running Pv3Rs for: AR-187 (summed MOI = 5).
## The episode number is:  1 2 3 4
## Number of valid relationship graphs (RGs) is 298
## =============================================================================|
## Computing log p(Y|RG) for 298 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-383...
## Running Pv3Rs for: AR-383 (summed MOI = 2).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-288...
## Running Pv3Rs for: AR-288 (summed MOI = 4).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-234...
## Running Pv3Rs for: AR-234 (summed MOI = 5).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 172
## =============================================================================|
## Computing log p(Y|RG) for 172 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-306...
## Running Pv3Rs for: AR-306 (summed MOI = 5).
## The episode number is:  1 2 3
## Number of valid relationship graphs (RGs) is 250
## =============================================================================|
## Computing log p(Y|RG) for 250 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-302...
## Running Pv3Rs for: AR-302 (summed MOI = 4).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-341...
## Running Pv3Rs for: AR-341 (summed MOI = 5).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 172
## =============================================================================|
## Computing log p(Y|RG) for 172 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-326...
## Running Pv3Rs for: AR-326 (summed MOI = 5).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 172
## =============================================================================|
## Computing log p(Y|RG) for 172 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-067...
## Running Pv3Rs for: AR-067 (summed MOI = 4).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-322...
## Running Pv3Rs for: AR-322 (summed MOI = 3).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-355...
## Running Pv3Rs for: AR-355 (summed MOI = 3).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-081...
## Running Pv3Rs for: AR-081 (summed MOI = 7).
## The episode number is:  1 2 3
## Number of valid relationship graphs (RGs) is 9773
## =============================================================================|
## Computing log p(Y|RG) for 9773 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-248...
## Running Pv3Rs for: AR-248 (summed MOI = 4).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-304...
## Running Pv3Rs for: AR-304 (summed MOI = 5).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 172
## =============================================================================|
## Computing log p(Y|RG) for 172 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-160...
## Running Pv3Rs for: AR-160 (summed MOI = 2).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-110...
## Running Pv3Rs for: AR-110 (summed MOI = 2).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-133...
## Running Pv3Rs for: AR-133 (summed MOI = 5).
## The episode number is:  1 2 3
## Number of valid relationship graphs (RGs) is 202
## =============================================================================|
## Computing log p(Y|RG) for 202 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-140...
## Running Pv3Rs for: AR-140 (summed MOI = 7).
## The episode number is:  1 2 3
## Number of valid relationship graphs (RGs) is 9773
## =============================================================================|
## Computing log p(Y|RG) for 9773 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-142...
## Running Pv3Rs for: AR-142 (summed MOI = 8).
## The episode number is:  1 2 3 4
## Number of valid relationship graphs (RGs) is 91237
## =============================================================================|
## Computing log p(Y|RG) for 91237 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-173...
## Running Pv3Rs for: AR-173 (summed MOI = 3).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-328...
## Running Pv3Rs for: AR-328 (summed MOI = 5).
## The episode number is:  1 2 3
## Number of valid relationship graphs (RGs) is 250
## =============================================================================|
## Computing log p(Y|RG) for 250 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-359...
## NOT Running Pv3Rs for: AR-359 because summed MOI exceeds threshold (observed = 11, MAX_MOI_TO_INCLUDE = 8).
## ID : AR-325...
## Running Pv3Rs for: AR-325 (summed MOI = 2).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-246...
## Running Pv3Rs for: AR-246 (summed MOI = 2).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-250...
## Running Pv3Rs for: AR-250 (summed MOI = 3).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-258...
## Running Pv3Rs for: AR-258 (summed MOI = 5).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 172
## =============================================================================|
## Computing log p(Y|RG) for 172 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-098...
## Running Pv3Rs for: AR-098 (summed MOI = 3).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-176...
## Running Pv3Rs for: AR-176 (summed MOI = 5).
## The episode number is:  1 2 3
## Number of valid relationship graphs (RGs) is 250
## =============================================================================|
## Computing log p(Y|RG) for 250 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-192...
## Running Pv3Rs for: AR-192 (summed MOI = 3).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-214...
## Running Pv3Rs for: AR-214 (summed MOI = 6).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 799
## =============================================================================|
## Computing log p(Y|RG) for 799 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-237...
## Running Pv3Rs for: AR-237 (summed MOI = 3).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-274...
## Running Pv3Rs for: AR-274 (summed MOI = 4).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-300...
## Running Pv3Rs for: AR-300 (summed MOI = 4).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-345...
## Running Pv3Rs for: AR-345 (summed MOI = 4).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-346...
## Running Pv3Rs for: AR-346 (summed MOI = 5).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 172
## =============================================================================|
## Computing log p(Y|RG) for 172 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-348...
## Running Pv3Rs for: AR-348 (summed MOI = 2).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
## ID : AR-382...
## Running Pv3Rs for: AR-382 (summed MOI = 2).
## The episode number is:  1 2
## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|
# End timer
t_end <- Sys.time()
cat("Pv3Rs for the whole dataset took : ", as.numeric(difftime(time1 = t_end, 
                                                               time2 = t_start, 
                                                               units = "secs"))/60, " mins", "\n", sep = "")
## Pv3Rs for the whole dataset took : 1.561027 mins
# Present the marginal data in a clearer format
indiv_posteriors_marginal <- do.call(rbind, 
                                     lapply(indiv_posteriors, function(x) {
                                       if (!is.null(x[["Pv3Rs"]])) {
                                         return(data.frame("subject_id"               = x[["subject_id"]], 
                                                           "episode_number"                     = x[["episode_number"]][-1], 
                                                           "Posterior_marginal_prob_C" = x[["Pv3Rs"]]$marg[, "C"], 
                                                           "Posterior_marginal_prob_L" = x[["Pv3Rs"]]$marg[, "L"], 
                                                           "Posterior_marginal_prob_I" = x[["Pv3Rs"]]$marg[, "I"]))
                                       } else {
                                         return(NULL)
                                       }
                                       
                                     }))
row.names(indiv_posteriors_marginal) <- 1:nrow(indiv_posteriors_marginal)

# Present the joint posterior estimates in a clearer format
indiv_posteriors_joint <- do.call(rbind, 
                                  lapply(indiv_posteriors, function(x) {
                                    if (!is.null(x[["Pv3Rs"]])) {
                                      joint_probs <- x[["Pv3Rs"]]$joint
                                      # Extract the state pairs and probabilities
                                      state_pairs <- names(joint_probs)
                                      prob_values <- as.numeric(joint_probs)
                                      
                                      # Create a data frame with subject_id, episode_number, and joint probabilities
                                      return(data.frame("subject_id"      = rep(x[["subject_id"]], length(state_pairs)), 
                                                        "episode_number"  = rep(x[["episode_number"]][-1], each = length(state_pairs)),
                                                        "state_pair"      = state_pairs, 
                                                        "joint_probability" = prob_values))
                                    } else {
                                      return(NULL)
                                    }
                                  }))
row.names(indiv_posteriors_joint) <- 1:nrow(indiv_posteriors_joint)

# Save Pv3Rs output because it's time consuming and 
# we don't want to re-run it every time.
save(list = c("RUN_EXAMPLE", "WITHIN_INDIVIDUAL_ALLELE_FREQ_THR", "BENCHMARK_MARKERS", "MAX_MOI_TO_INCLUDE", "PRIOR_3RS", 
              "analysis_data", "baseline_fs", 
              "indiv_posteriors", "indiv_posteriors_marginal", "indiv_posteriors_joint"), 
     file = paste0("./outputs/Pv3Rs_sols_posteriors_", 
                   strftime(Sys.time(), format = "%Y%m%d_%H%M%S"), 
                   ".RData"))

Explore the posterior

The marginal probabilities give us the probability of the three states for each recurrent episode. However, this does not consider the joint probability of different states when a person experienced more than one recurrent episode.

indiv_posteriors_marginal
##    subject_id episode_number Posterior_marginal_prob_C
## 1      AR-024              2                 0.0000000
## 2      AR-024              3                 0.7576302
## 3      AR-024              4                 0.8623076
## 4      AR-024              5                 0.0000000
## 5      AR-221              2                 0.0000000
## 6      AR-343              2                 0.0000000
## 7      AR-366              2                 0.0000000
## 8      AR-187              2                 0.7721759
## 9      AR-187              3                 0.7721759
## 10     AR-187              4                 0.0000000
## 11     AR-383              2                 0.0000000
## 12     AR-288              2                 0.0000000
## 13     AR-234              2                 0.0000000
## 14     AR-306              2                 0.0000000
## 15     AR-306              3                 0.7913832
## 16     AR-302              2                 0.0000000
## 17     AR-341              2                 0.0000000
## 18     AR-326              2                 0.0000000
## 19     AR-067              2                 0.0000000
## 20     AR-322              2                 0.0000000
## 21     AR-355              2                 0.0000000
## 22     AR-081              2                 0.0000000
## 23     AR-081              3                 0.0000000
## 24     AR-248              2                 0.0000000
## 25     AR-304              2                 0.0000000
## 26     AR-160              2                 0.7485703
## 27     AR-110              2                 0.7479272
## 28     AR-133              2                 0.8615406
## 29     AR-133              3                 0.0000000
## 30     AR-140              2                 0.0000000
## 31     AR-140              3                 0.0000000
## 32     AR-142              2                 0.0000000
## 33     AR-142              3                 0.0000000
## 34     AR-142              4                 0.0000000
## 35     AR-173              2                 0.0000000
## 36     AR-328              2                 0.0000000
## 37     AR-328              3                 0.0000000
## 38     AR-325              2                 0.0000000
## 39     AR-246              2                 0.0000000
## 40     AR-250              2                 0.6878086
## 41     AR-258              2                 0.0000000
## 42     AR-098              2                 0.0000000
## 43     AR-176              2                 0.0000000
## 44     AR-176              3                 0.0000000
## 45     AR-192              2                 0.6883825
## 46     AR-214              2                 0.0000000
## 47     AR-237              2                 0.6339993
## 48     AR-274              2                 0.0000000
## 49     AR-300              2                 0.0000000
## 50     AR-345              2                 0.0000000
## 51     AR-346              2                 0.8462352
## 52     AR-348              2                 0.7479272
## 53     AR-382              2                 0.7451339
##    Posterior_marginal_prob_L Posterior_marginal_prob_I
## 1                 0.20029039              7.997096e-01
## 2                 0.24236975              1.780460e-08
## 3                 0.13769233              2.870620e-08
## 4                 0.04708870              9.529113e-01
## 5                 0.99999919              8.095861e-07
## 6                 0.09761816              9.023818e-01
## 7                 0.49131418              5.086858e-01
## 8                 0.22782070              3.361901e-06
## 9                 0.22782283              1.232510e-06
## 10                0.15668331              8.433167e-01
## 11                0.26708459              7.329154e-01
## 12                0.09302359              9.069764e-01
## 13                0.95411195              4.588805e-02
## 14                0.10514624              8.948538e-01
## 15                0.20861539              1.457631e-06
## 16                0.62091008              3.790899e-01
## 17                0.05713046              9.428695e-01
## 18                0.89692628              1.030737e-01
## 19                0.99712108              2.878920e-03
## 20                0.20073604              7.992640e-01
## 21                0.18443518              8.155648e-01
## 22                0.34465510              6.553449e-01
## 23                0.99999318              6.820487e-06
## 24                0.99999371              6.292255e-06
## 25                0.05497134              9.450287e-01
## 26                0.25142969              2.054618e-08
## 27                0.25207193              8.834381e-07
## 28                0.13805319              4.061912e-04
## 29                0.99995771              4.228845e-05
## 30                0.31585812              6.841419e-01
## 31                0.99847214              1.527856e-03
## 32                0.09188394              9.081161e-01
## 33                0.87083346              1.291665e-01
## 34                0.99922528              7.747186e-04
## 35                0.85959432              1.404057e-01
## 36                0.22064240              7.793576e-01
## 37                0.31818593              6.818141e-01
## 38                0.99994032              5.967860e-05
## 39                0.26710484              7.328952e-01
## 40                0.31219012              1.300991e-06
## 41                0.06281198              9.371880e-01
## 42                0.99999479              5.206603e-06
## 43                0.25588975              7.441102e-01
## 44                0.99999554              4.459706e-06
## 45                0.31161734              1.483804e-07
## 46                0.71125930              2.887407e-01
## 47                0.36517961              8.210690e-04
## 48                0.09302472              9.069753e-01
## 49                0.09302334              9.069767e-01
## 50                0.09507480              9.049252e-01
## 51                0.15376481              2.089266e-11
## 52                0.25207193              8.834381e-07
## 53                0.25485692              9.180472e-06

The joint probabilites give us values for each possible combination of states, depending on the number of recurrent episodes experienced by the participant.

joint_summary <- indiv_posteriors_joint %>% 
                    group_by(subject_id, state_pair, joint_probability) %>% 
                    filter(episode_number == max(episode_number)) %>% 
                    mutate(percentage = round(joint_probability*100, 3),
                           total_recurrences = episode_number-1) %>%
                    select(-episode_number) %>% 
                    arrange(subject_id, total_recurrences, percentage) %>% 
                    relocate(total_recurrences, .before = state_pair)

joint_summary
## # A tibble: 282 × 5
## # Groups:   subject_id, state_pair, joint_probability [282]
##    subject_id total_recurrences state_pair joint_probability percentage
##    <chr>                  <dbl> <chr>                  <dbl>      <dbl>
##  1 AR-024                     4 CCCC                       0          0
##  2 AR-024                     4 LCCC                       0          0
##  3 AR-024                     4 ICCC                       0          0
##  4 AR-024                     4 CLCC                       0          0
##  5 AR-024                     4 LLCC                       0          0
##  6 AR-024                     4 ILCC                       0          0
##  7 AR-024                     4 CICC                       0          0
##  8 AR-024                     4 LICC                       0          0
##  9 AR-024                     4 IICC                       0          0
## 10 AR-024                     4 CCLC                       0          0
## # ℹ 272 more rows

Plot marginal probabilities

marginal_summary <- indiv_posteriors_marginal %>% 
  pivot_longer(cols = !subject_id & !episode_number, 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_classification = case_when(posterior_type == "Posterior_marginal_prob_C" ~ "Recrudescence",
                                              posterior_type == "Posterior_marginal_prob_L" ~ "Relapse",
                                              posterior_type == "Posterior_marginal_prob_I" ~ "Reinfection"),
         posterior_classification = factor(posterior_classification, 
                                           levels = c("Relapse", "Recrudescence", "Reinfection"))) %>% 
  select(-posterior_type)

marginal_summary
## # A tibble: 159 × 4
##    subject_id episode_number posterior_value posterior_classification
##    <chr>               <int>           <dbl> <fct>                   
##  1 AR-024                  2    0            Recrudescence           
##  2 AR-024                  2    0.200        Relapse                 
##  3 AR-024                  2    0.800        Reinfection             
##  4 AR-024                  3    0.758        Recrudescence           
##  5 AR-024                  3    0.242        Relapse                 
##  6 AR-024                  3    0.0000000178 Reinfection             
##  7 AR-024                  4    0.862        Recrudescence           
##  8 AR-024                  4    0.138        Relapse                 
##  9 AR-024                  4    0.0000000287 Reinfection             
## 10 AR-024                  5    0            Recrudescence           
## # ℹ 149 more rows

By episode number

marginal_summary %>% 
  group_by(subject_id) %>%
  arrange(desc(posterior_value), .by_group = TRUE) %>%
  mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>%
  
  ggplot(aes(x = factor(episode_number), y = posterior_value, group = episode_number, fill = posterior_classification)) + 
    geom_bar(stat = "identity", position = "fill") +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Episode number", 
         y     = "Posterior probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~subject_id)

By treatment arm

marginal_summary %>% 
  left_join(analysis_data %>% select(subject_id, treatment_arm) %>% distinct(), 
            by = "subject_id") %>% 
  group_by(subject_id) %>% 
  arrange(desc(posterior_value), .by_group = TRUE) %>% 
  mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>% 
  ggplot(aes(x = factor(episode_number), y = posterior_value, group = episode_number, fill = posterior_classification)) + 
    geom_bar(stat = "identity", position = "fill") +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
  
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Episode number", 
         y     = "Posterior probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(treatment_arm ~ subject_id)

By days since last episode

marginal_summary %>% 
  left_join(analysis_data %>% select(subject_id, episode_number, treatment_arm, visit_date, days_since_enrolment, days_since_last_episode) %>% distinct(), 
            by = c("subject_id", "episode_number")) %>% 
  group_by(subject_id) %>%
  arrange(desc(posterior_value), .by_group = TRUE) %>%
  mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>%
  ggplot(aes(x = days_since_enrolment, y = posterior_value, group = episode_number, fill = posterior_classification)) + 
  geom_bar(stat = "identity", position = "fill", width = 3) +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
  
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
  labs(x     = "Days since enrolment", 
       y     = "Posterior probability", 
       fill = "") + 
  theme_bw() +
  facet_wrap(~subject_id)
## Warning: `position_stack()` requires non-overlapping x intervals.

Plot joint probabilities

pal1 <- c(
  "C" = "turquoise3", 
  "I" = "magenta3", 
  "L" = "skyblue4")

pal2 <- c(
  "II" = "magenta3",
  "IL" = "darkorange2",
  "LI" = "goldenrod2",
  "LL" = "skyblue4",
  "CI" = "saddlebrown",
  "CL" = "turquoise4",
  "IC" = "darkolivegreen",
  "LC" = "lightseagreen"
)

pal3 <- c(
  "IIL" = "darkorange3",
  "ILL" = "orangered3",
  "LIL" = "goldenrod3",
  "LLL" = "skyblue4",
  "CCI" = "sienna4",
  "CCL" = "cadetblue3",
  "CLI" = "darkcyan",
  "CLL" = "steelblue3",
  "LCI" = "peru",
  "LCL" = "powderblue",
  "LLI" = "orange3"
)

pal4 <- c(
  "ICCI" = "saddlebrown",
  "ICCL" = "darkseagreen",
  "ICLI" = "orange3",
  "ICLL" = "lightsteelblue",
  "ILCI" = "darkkhaki",
  "ILCL" = "skyblue3",
  "ILLI" = "gold3",
  "LCCI" = "rosybrown",
  "LCCL" = "lightblue3",
  "LCLI" = "goldenrod3",
  "LCLL" = "deepskyblue3",
  "LLCI" = "tan",
  "LLCL" = "mediumturquoise",
  "LLLI" = "darkorange3"
)

combined_pal <- c(pal1, pal2, pal3, pal4)
plotJointProb <- function(data, recurrence_n, prob_threshold, color_palette, ...){
  data %>% 
    filter(total_recurrences == recurrence_n, joint_probability > prob_threshold) %>% 
    group_by(subject_id) %>% 
    mutate(state_pair = fct_reorder(state_pair, joint_probability)) %>%
    ungroup() %>% 
    ggplot(aes(x = joint_probability, 
               y = subject_id, 
               fill = state_pair)) +
      geom_bar(position = "stack", stat = "identity") +
      scale_x_continuous(expand = c(0, 0)) +
      scale_y_discrete(expand = c(0, 0)) +
      scale_fill_manual(values = color_palette) +
      labs(x = "Joint probability estimate",
           y = "Participant",
           fill = "Classification state") +
      theme_bw() + 
    facet_wrap(~total_recurrences, scales = "free_y")
}

By total number of recurrences

plot_joint1 <- plotJointProb(joint_summary, 1, 0, pal1) 
plot_joint2 <- plotJointProb(joint_summary, 2, 0, pal2) + guides(fill = guide_legend(ncol = 2)) 
plot_joint3 <- plotJointProb(joint_summary, 3, 0.001, pal3) + guides(fill = guide_legend(ncol = 3)) 
plot_joint4 <- plotJointProb(joint_summary, 4, 0.001, pal4) + guides(fill = guide_legend(ncol = 3)) 

(plot_joint1 | plot_joint2) / (plot_joint3 | plot_joint4) +
  plot_annotation(subtitle = "I = Reinfection, L = Relapse, C = Recrudescence",
                  caption = expression(italic("For >2 recurrences, only probability estimates > 0.001 are shown"))) +
  plot_layout(heights = c(2, 1))

By treatment arm and number of recurrences

joint_summary %>% 
    left_join(analysis_data %>% select(subject_id, treatment_arm) %>% distinct(),
              by = "subject_id") %>% 
    filter(joint_probability > 0.001) %>% 
    group_by(subject_id) %>% 
    mutate(state_pair = fct_reorder(state_pair, joint_probability)) %>%
    ungroup() %>% 
    ggplot(aes(x = joint_probability, 
               y = reorder(subject_id, total_recurrences), 
               fill = state_pair)) +
      geom_bar(position = "stack", stat = "identity") +
      scale_x_continuous(expand = c(0, 0)) +
      scale_y_discrete(expand = c(0, 0)) +
      scale_fill_manual(values = combined_pal) +
      labs(x = "Joint probability estimate",
           y = "Participant",
           fill = "Classification state",
           subtitle = "I = Reinfection, L = Relapse, C = Recrudescence",
           caption = expression(italic("Color coding: if > 1 recurrence, blue shades denote combinations of relapse and recrudescence (L and C), shades of orange/browns denote \ncombos of reinfection and relapse (I and L) and green shades denote combos of reinfection with recrudescence (I and C)"))) +
      theme_bw() + 
      facet_wrap(treatment_arm~total_recurrences, scales = "free") +
      # facet_wrap(total_recurrences~treatment_arm, scales = "free") +
      theme(plot.caption = element_text(hjust = 0, vjust = -2)) 

‘Confidence’ in highest joint probability estimate

What is the joint probability estimate for the ‘highest ranking’ state pair classification? I.e. if >80% relatively high confidence? n=19

joint_summary %>% 
  filter(joint_probability > 0.001) %>% 
  group_by(subject_id) %>% 
  # get the highest prob for each subj and store ranking
  mutate(ranking = rank(-joint_probability, ties.method = "first")) %>% 
  arrange(subject_id, ranking) %>% 
  filter(ranking == 1) %>% 
  ggplot(aes(x = reorder(subject_id, -joint_probability),
             y = joint_probability,
             fill = factor(total_recurrences))) +
    geom_col() +
    geom_hline(yintercept = 0.8, color = "darkgray", linetype = "dashed") +
    labs(x = "Participant",
         y = "Joint probability estimate for highest 'ranking' state pair",
         fill = "Number of recurrent episodes",
         caption = expression(italic("Dashed line indicates an arbitrary threshold of 80%"))) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

How many state pairs make up the arbitrary 80% threshold?

joint_summary %>% 
  filter(joint_probability > 0.001) %>% 
  group_by(subject_id) %>% 
  # Get the highest prob for each subject and store ranking
  mutate(ranking = rank(-joint_probability, ties.method = "first")) %>% 
  # reverse ranking order of ranking so we plot the highest rank at the bottom
  arrange(subject_id, desc(ranking)) %>%
  mutate(ranking = factor(ranking, levels = unique(ranking))) %>% 
  ggplot(aes(x = subject_id,
             y = joint_probability,
             fill = ranking)) +
    geom_bar(stat = "identity", position = "stack") +
    geom_hline(yintercept = 0.8, color = "darkgray", linetype = "dashed") +
    scale_fill_manual(values = colorRampPalette(brewer.pal(12, "Paired"))(14),
                      guide = guide_legend(reverse = T)) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x = "Participant",
         y = "Joint probability estimate for state pair",
         fill = "Number of possible \nestimated state pairs",
         caption = expression(italic("Dashed line indicates an arbitrary threshold of 80%"))) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
    facet_wrap(~total_recurrences, scales = "free_x")

Highest ranking classification by episode number

joint_top3_summary <- joint_summary %>% 
            select(-percentage) %>% 
            group_by(subject_id) %>% 
            # Get the highest prob for each subject and store ranking
            mutate(ranking = rank(-joint_probability, ties.method = "first")) %>% 
            arrange(subject_id, ranking) %>% 
            filter(ranking %in% c(1, 2, 3))

We will keep the ‘highest ranking’ joint probability estimate so we can compare the state classification for each episode with other metrics

joint_recurrence_summary <- joint_top3_summary %>% 
  select(-total_recurrences) %>% 
  filter(ranking == 1) %>% 
  mutate(state_pair_split = strsplit(state_pair, "")) %>%  # Split state_pair into individual letters
  unnest(state_pair_split) %>%  # Unnest to create a new row for each letter
  group_by(subject_id) %>%
  mutate(
    episode_number = row_number() + 1,  # Create episode number starting from n+1 to reflect number of recurrence
    joint_probability = joint_probability,  # Keep the same joint_probability for all episodes\
    state_classification = case_when(state_pair_split == "L" ~ "Relapse",
                                     state_pair_split == "C" ~ "Recrudescence",
                                     state_pair_split == "I" ~ "Reinfection")) %>%
  select(subject_id, episode_number, state_classification, joint_probability)
joint_recurrence_summary %>% 
  ggplot(aes(x = factor(episode_number), y = joint_probability, fill = state_classification)) +
    geom_col() +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Episode number", 
         y     = "Posterior joint probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~subject_id)

Comparison marginal vs joint

posterior_summary <- marginal_summary %>% 
  rename("marginal_posterior_probability" = "posterior_value",
         "marginal_classification" = "posterior_classification") %>% 
  left_join(joint_recurrence_summary %>% 
               rename("joint_classification" = "state_classification",
                      "joint_posterior_probability" = "joint_probability"),
             by = c("subject_id", "episode_number")) %>% 
  pivot_longer(cols = c(marginal_posterior_probability, 
                        joint_posterior_probability, 
                        marginal_classification, 
                        joint_classification),
    names_to = c("estimate_type", ".value"),
    names_pattern = "(marginal|joint)_(posterior_probability|classification)") %>% 
  distinct() 
posterior_summary %>% 
  group_by(subject_id, episode_number) %>%
  arrange(desc(posterior_probability), .by_group = TRUE) %>%
  mutate(classification = factor(classification, levels = unique(classification))) %>%
  ungroup() %>% 
  ggplot(aes(x = factor(episode_number), y = posterior_probability, group = episode_number)) + 
    geom_bar(data = . %>% filter(estimate_type == "marginal"),
             aes(fill = classification),
             stat = "identity", 
             position = "fill") +
    geom_segment(data = . %>% filter(estimate_type == "joint"),
               aes(x = as.numeric(factor(episode_number)) - 0.4,  
                   xend = as.numeric(factor(episode_number)) + 0.4,  
                   y = posterior_probability,  
                   yend = posterior_probability, 
                   color = classification),
               size = 1.5) +
    geom_point(data = . %>% filter(estimate_type == "joint"),
               aes(color = classification,
                   fill = classification),
               shape = 21) +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_color_manual(values = c("Relapse" = "#006064",
                                 "Recrudescence" = "#3B4F75",
                                 "Reinfection" = "#8B008B")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Episode number", 
         y     = "Posterior probability", 
         fill = "Marginal classification",
         color = "Joint probability estimate \n and classification") + 
    theme_bw() +
    facet_wrap(~subject_id)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Comparison to IBS and IBD estimates

sols_pnh <- read.csv(here("data/final", "sols_PNH_data.csv"))
ibd_r_data <- readRDS(here("data/final", "sols_all_meta.rds"))
ibd_rhat_data <- readRDS(here("data/final", "sols_sig_meta.rds"))

For direct comparison with the we can only look at the paired comparisons between baseline samples and follow-up samples, but not other pw comparisons for a given participant.

sols_ibd <- ibd_r_data %>% 
              # filter to include only paired samples, and only pw comparisons to baseline
              filter(comparison_type == "paired", day_id2 == 0) %>% 
              mutate(subject_id = paste0("AR-", patientid1),
                     day_id1 = as.integer(day_id1),
                     day_id2 = as.integer(day_id2)) %>% 
              group_by(subject_id) %>%  
              arrange(day_id1) %>%  
              # create episode_number accounting for baseline as episode 1
              mutate(episode_number = row_number() + 1) %>% 
              ungroup()
classification_summary <- indiv_posteriors_marginal %>% 
  rename("posterior_probability_recrudescence" = "Posterior_marginal_prob_C",
         "posterior_probability_relapse" = "Posterior_marginal_prob_L",
         "posterior_probability_reinfection" = "Posterior_marginal_prob_I") %>% 
  # merge with epi variables
  left_join(analysis_data %>% select(subject_id, sample_id, episode_number, treatment_arm, visit_date, days_since_enrolment, days_since_last_episode) %>% distinct(), 
            by = c("subject_id", "episode_number")) %>% 
  # merge with PNH data
  left_join(sols_pnh, by = c("subject_id" = "Patient", "episode_number" = "recurrence")) %>% 
  rename("prop_same_haps_1minusPNH" = "Prop_het", 
         "pnh_range" = "identity", 
         "classification" = "Classification") %>% 
  # create bins for 1-PNH (IBS metric)
  mutate(ibs_range = cut(prop_same_haps_1minusPNH,  
                         breaks = c(0, 0.25, 0.5, 0.75, 1), 
                         include.lowest = TRUE, 
                         labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  # merge with IBD r
  left_join(sols_ibd,
            by = c("subject_id", "episode_number")) %>% 
  # create bins for IBD r
  mutate(ibd_range = cut(estimate,  
                         breaks = c(0, 0.25, 0.5, 0.75, 1), 
                         include.lowest = TRUE, 
                         labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  # merge with joint probabilities
  left_join(joint_recurrence_summary %>% 
              rename("joint_classification" = "state_classification"),
            by = c("subject_id", "episode_number"))

classification_summary
##    subject_id episode_number posterior_probability_recrudescence
## 1      AR-024              2                           0.0000000
## 2      AR-024              3                           0.7576302
## 3      AR-024              4                           0.8623076
## 4      AR-024              5                           0.0000000
## 5      AR-221              2                           0.0000000
## 6      AR-343              2                           0.0000000
## 7      AR-366              2                           0.0000000
## 8      AR-187              2                           0.7721759
## 9      AR-187              3                           0.7721759
## 10     AR-187              4                           0.0000000
## 11     AR-383              2                           0.0000000
## 12     AR-288              2                           0.0000000
## 13     AR-234              2                           0.0000000
## 14     AR-306              2                           0.0000000
## 15     AR-306              3                           0.7913832
## 16     AR-302              2                           0.0000000
## 17     AR-341              2                           0.0000000
## 18     AR-326              2                           0.0000000
## 19     AR-067              2                           0.0000000
## 20     AR-322              2                           0.0000000
## 21     AR-355              2                           0.0000000
## 22     AR-081              2                           0.0000000
## 23     AR-081              3                           0.0000000
## 24     AR-248              2                           0.0000000
## 25     AR-304              2                           0.0000000
## 26     AR-160              2                           0.7485703
## 27     AR-110              2                           0.7479272
## 28     AR-133              2                           0.8615406
## 29     AR-133              3                           0.0000000
## 30     AR-140              2                           0.0000000
## 31     AR-140              3                           0.0000000
## 32     AR-142              2                           0.0000000
## 33     AR-142              3                           0.0000000
## 34     AR-142              4                           0.0000000
## 35     AR-173              2                           0.0000000
## 36     AR-328              2                           0.0000000
## 37     AR-328              3                           0.0000000
## 38     AR-325              2                           0.0000000
## 39     AR-246              2                           0.0000000
## 40     AR-250              2                           0.6878086
## 41     AR-258              2                           0.0000000
## 42     AR-098              2                           0.0000000
## 43     AR-176              2                           0.0000000
## 44     AR-176              3                           0.0000000
## 45     AR-192              2                           0.6883825
## 46     AR-214              2                           0.0000000
## 47     AR-237              2                           0.6339993
## 48     AR-274              2                           0.0000000
## 49     AR-300              2                           0.0000000
## 50     AR-345              2                           0.0000000
## 51     AR-346              2                           0.8462352
## 52     AR-348              2                           0.7479272
## 53     AR-382              2                           0.7451339
##    posterior_probability_relapse posterior_probability_reinfection  sample_id
## 1                     0.20029039                      7.997096e-01  AR-024-56
## 2                     0.24236975                      1.780460e-08  AR-024-70
## 3                     0.13769233                      2.870620e-08  AR-024-84
## 4                     0.04708870                      9.529113e-01 AR-024-112
## 5                     0.99999919                      8.095861e-07  AR-221-70
## 6                     0.09761816                      9.023818e-01 AR-343-112
## 7                     0.49131418                      5.086858e-01 AR-366-168
## 8                     0.22782070                      3.361901e-06  AR-187-42
## 9                     0.22782283                      1.232510e-06  AR-187-84
## 10                    0.15668331                      8.433167e-01 AR-187-168
## 11                    0.26708459                      7.329154e-01 AR-383-168
## 12                    0.09302359                      9.069764e-01  AR-288-70
## 13                    0.95411195                      4.588805e-02 AR-234-112
## 14                    0.10514624                      8.948538e-01  AR-306-84
## 15                    0.20861539                      1.457631e-06  AR-306-85
## 16                    0.62091008                      3.790899e-01  AR-302-70
## 17                    0.05713046                      9.428695e-01  AR-341-84
## 18                    0.89692628                      1.030737e-01 AR-326-168
## 19                    0.99712108                      2.878920e-03  AR-067-42
## 20                    0.20073604                      7.992640e-01 AR-322-140
## 21                    0.18443518                      8.155648e-01 AR-355-140
## 22                    0.34465510                      6.553449e-01 AR-081-140
## 23                    0.99999318                      6.820487e-06 AR-081-168
## 24                    0.99999371                      6.292255e-06  AR-248-84
## 25                    0.05497134                      9.450287e-01  AR-304-28
## 26                    0.25142969                      2.054618e-08  AR-160-84
## 27                    0.25207193                      8.834381e-07  AR-110-84
## 28                    0.13805319                      4.061912e-04  AR-133-84
## 29                    0.99995771                      4.228845e-05 AR-133-140
## 30                    0.31585812                      6.841419e-01  AR-140-56
## 31                    0.99847214                      1.527856e-03  AR-140-70
## 32                    0.09188394                      9.081161e-01  AR-142-42
## 33                    0.87083346                      1.291665e-01  AR-142-46
## 34                    0.99922528                      7.747186e-04  AR-142-97
## 35                    0.85959432                      1.404057e-01  AR-173-28
## 36                    0.22064240                      7.793576e-01  AR-328-84
## 37                    0.31818593                      6.818141e-01 AR-328-112
## 38                    0.99994032                      5.967860e-05 AR-325-140
## 39                    0.26710484                      7.328952e-01  AR-246-70
## 40                    0.31219012                      1.300991e-06  AR-250-56
## 41                    0.06281198                      9.371880e-01 AR-258-140
## 42                    0.99999479                      5.206603e-06  AR-098-56
## 43                    0.25588975                      7.441102e-01  AR-176-28
## 44                    0.99999554                      4.459706e-06  AR-176-33
## 45                    0.31161734                      1.483804e-07  AR-192-70
## 46                    0.71125930                      2.887407e-01  AR-214-64
## 47                    0.36517961                      8.210690e-04  AR-237-70
## 48                    0.09302472                      9.069753e-01  AR-274-56
## 49                    0.09302334                      9.069767e-01  AR-300-84
## 50                    0.09507480                      9.049252e-01 AR-345-112
## 51                    0.15376481                      2.089266e-11 AR-346-112
## 52                    0.25207193                      8.834381e-07  AR-348-84
## 53                    0.25485692                      9.180472e-06 AR-382-168
##    treatment_arm visit_date days_since_enrolment days_since_last_episode
## 1          AL-PQ 2017-12-11                   56                      56
## 2          AL-PQ 2017-12-25                   70                      14
## 3          AL-PQ 2018-01-09                   85                      15
## 4          AL-PQ 2018-02-05                  112                      27
## 5          AL-PQ 2018-11-15                   70                      70
## 6         DHP-PQ 2019-05-08                  112                     112
## 7         DHP-PQ 2019-07-24                  168                     168
## 8             AL 2018-09-27                   43                      43
## 9             AL 2018-11-07                   84                      41
## 10            AL 2019-01-30                  168                      84
## 11        DHP-PQ 2019-08-07                  168                     168
## 12        DHP-PQ 2019-01-08                   70                      70
## 13        DHP-PQ 2019-01-04                  112                     112
## 14         AL-PQ 2019-02-18                   84                      84
## 15         AL-PQ 2019-02-19                   85                       1
## 16        DHP-PQ 2019-01-23                   70                      70
## 17        DHP-PQ 2019-04-08                   84                      84
## 18        DHP-PQ 2019-06-26                  168                     168
## 19            AL 2018-01-22                   42                      42
## 20        DHP-PQ 2019-05-28                  140                     140
## 21         AL-PQ 2019-06-13                  140                     140
## 22         AL-PQ 2018-07-16                  140                     140
## 23         AL-PQ 2018-08-13                  168                      28
## 24         AL-PQ 2018-12-22                   82                      82
## 25            AL 2018-12-17                   28                      28
## 26            AL 2018-10-16                   84                      84
## 27         AL-PQ 2018-08-07                   84                      84
## 28            AL 2018-09-10                   84                      84
## 29            AL 2018-11-05                  140                      56
## 30         AL-PQ 2018-08-20                   56                      56
## 31         AL-PQ 2018-09-03                   70                      14
## 32            AL 2018-08-09                   42                      42
## 33            AL 2018-08-13                   46                       4
## 34            AL 2018-10-03                   97                      51
## 35            AL 2018-09-03                   28                      28
## 36        DHP-PQ 2019-04-01                   82                      82
## 37        DHP-PQ 2019-05-02                  113                      31
## 38            AL 2019-05-29                  140                     140
## 39        DHP-PQ 2018-12-05                   70                      70
## 40            AL 2018-11-26                   56                      56
## 41        DHP-PQ 2019-02-25                  140                     140
## 42         AL-PQ 2018-06-14                   56                      56
## 43            AL 2018-09-05                   28                      28
## 44            AL 2018-09-10                   33                       5
## 45         AL-PQ 2018-10-29                   70                      70
## 46         AL-PQ 2018-11-07                   64                      64
## 47         AL-PQ 2018-11-26                   70                      70
## 48         AL-PQ 2018-12-12                   56                      56
## 49         AL-PQ 2019-02-04                   84                      84
## 50         AL-PQ 2019-05-08                  112                     112
## 51        DHP-PQ 2019-05-08                  112                     112
## 52            AL 2019-04-11                   84                      84
## 53         AL-PQ 2019-08-06                  168                     168
##        sample       date treatment treatment2 prop_same_haps_1minusPNH
## 1   AR-024-56 2017-12-11     AL-PQ         PQ                0.3068233
## 2   AR-024-70 2017-12-25     AL-PQ         PQ                0.2219938
## 3   AR-024-84 2018-01-09     AL-PQ         PQ                0.2414539
## 4  AR-024-112 2018-02-05     AL-PQ         PQ                0.1420881
## 5   AR-221-70 2018-11-15     AL-PQ         PQ                1.0000000
## 6  AR-343-112 2019-05-08    DHP-PQ         PQ                0.4906786
## 7  AR-366-168 2019-07-24    DHP-PQ         PQ                0.4117348
## 8   AR-187-42 2018-09-27        AL         AL                1.0000000
## 9   AR-187-84 2018-11-07        AL         AL                1.0000000
## 10 AR-187-168 2019-01-30        AL         AL                0.3852387
## 11 AR-383-168 2019-08-07    DHP-PQ         PQ                0.2490546
## 12  AR-288-70 2019-01-08    DHP-PQ         PQ                0.3338144
## 13 AR-234-112 2019-01-04    DHP-PQ         PQ                0.8279783
## 14  AR-306-84 2019-02-18     AL-PQ         PQ                0.7195963
## 15  AR-306-85 2019-02-19     AL-PQ         PQ                0.7195963
## 16  AR-302-70 2019-01-23    DHP-PQ         PQ                0.8824741
## 17  AR-341-84 2019-04-08    DHP-PQ         PQ                0.4394276
## 18 AR-326-168 2019-06-26    DHP-PQ         PQ                0.8963978
## 19  AR-067-42 2018-01-22        AL         AL                1.0000000
## 20 AR-322-140 2019-05-28    DHP-PQ         PQ                0.3955032
## 21 AR-355-140 2019-06-13     AL-PQ         PQ                0.3345985
## 22 AR-081-140 2018-07-16     AL-PQ         PQ                0.6640732
## 23 AR-081-168 2018-08-13     AL-PQ         PQ                0.6640732
## 24  AR-248-84 2018-12-22     AL-PQ         PQ                1.0000000
## 25  AR-304-28 2018-12-17        AL         AL                0.5245581
## 26  AR-160-84 2018-10-16        AL         AL                1.0000000
## 27  AR-110-84 2018-08-07     AL-PQ         PQ                1.0000000
## 28  AR-133-84 2018-09-10        AL         AL                1.0000000
## 29 AR-133-140 2018-11-05        AL         AL                1.0000000
## 30  AR-140-56 2018-08-20     AL-PQ         PQ                0.8158586
## 31  AR-140-70 2018-09-03     AL-PQ         PQ                1.0000000
## 32  AR-142-42 2018-08-09        AL         AL                0.3976700
## 33  AR-142-46 2018-08-13        AL         AL                0.4526802
## 34  AR-142-97 2018-10-03        AL         AL                0.7944225
## 35  AR-173-28 2018-09-03        AL         AL                0.8550821
## 36  AR-328-84 2019-04-01    DHP-PQ         PQ                0.4645095
## 37 AR-328-112 2019-05-02    DHP-PQ         PQ                0.6741595
## 38 AR-325-140 2019-05-29        AL         AL                0.8956478
## 39  AR-246-70 2018-12-05    DHP-PQ         PQ                0.2168451
## 40  AR-250-56 2018-11-26        AL         AL                1.0000000
## 41 AR-258-140 2019-02-25    DHP-PQ         PQ                0.8162594
## 42  AR-098-56 2018-06-14     AL-PQ         PQ                1.0000000
## 43  AR-176-28 2018-09-05        AL         AL                0.4092557
## 44  AR-176-33 2018-09-10        AL         AL                0.3871925
## 45  AR-192-70 2018-10-29     AL-PQ         PQ                1.0000000
## 46  AR-214-64 2018-11-07     AL-PQ         PQ                0.9085737
## 47  AR-237-70 2018-11-26     AL-PQ         PQ                1.0000000
## 48  AR-274-56 2018-12-12     AL-PQ         PQ                0.1967818
## 49  AR-300-84 2019-02-04     AL-PQ         PQ                0.2981501
## 50 AR-345-112 2019-05-08     AL-PQ         PQ                0.3738918
## 51 AR-346-112 2019-05-08    DHP-PQ         PQ                1.0000000
## 52  AR-348-84 2019-04-11        AL         AL                1.0000000
## 53 AR-382-168 2019-08-06     AL-PQ         PQ                1.0000000
##    delay_since_prev_ep change_moi max_moi clones pnh_range classification
## 1                   56          1       2   poly  (0,0.75]     Homologous
## 2                   70         -1       1   mono  (0.75,1]   Heterologous
## 3                   85          0       1   mono  (0.75,1]   Heterologous
## 4                  112          1       2   poly  (0.75,1]   Heterologous
## 5                   70          1       2   poly  (-Inf,0]     Homologous
## 6                  112          1       3   poly  (0,0.75]     Homologous
## 7                  168         -2       1   mono  (0,0.75]     Homologous
## 8                   43          0       1   mono  (-Inf,0]     Homologous
## 9                   84          0       1   mono  (-Inf,0]     Homologous
## 10                 168          1       2   poly  (0,0.75]     Homologous
## 11                 168          0       1   mono  (0.75,1]   Heterologous
## 12                  70          0       2   poly  (0,0.75]     Homologous
## 13                 112         -1       2   poly  (0,0.75]     Homologous
## 14                  84          0       2   poly  (0,0.75]     Homologous
## 15                  85         -1       1   mono  (0,0.75]     Homologous
## 16                  70          0       2   poly  (0,0.75]     Homologous
## 17                  84          1       3   poly  (0,0.75]     Homologous
## 18                 168         -1       2   poly  (0,0.75]     Homologous
## 19                  42          0       2   poly  (-Inf,0]     Homologous
## 20                 140          1       2   poly  (0,0.75]     Homologous
## 21                 140          1       2   poly  (0,0.75]     Homologous
## 22                 140          0       2   poly  (0,0.75]     Homologous
## 23                 168          1       3   poly  (0,0.75]     Homologous
## 24                  82          0       2   poly  (-Inf,0]     Homologous
## 25                  28         -1       2   poly  (0,0.75]     Homologous
## 26                  84          0       1   mono  (-Inf,0]     Homologous
## 27                  84          0       1   mono  (-Inf,0]     Homologous
## 28                  84          0       1   mono  (-Inf,0]     Homologous
## 29                 140          2       3   poly  (-Inf,0]     Homologous
## 30                  56          1       3   poly  (0,0.75]     Homologous
## 31                  70         -1       2   poly  (-Inf,0]     Homologous
## 32                  42          1       3   poly  (0,0.75]     Homologous
## 33                  46         -2       1   mono  (0,0.75]     Homologous
## 34                  97          1       2   poly  (0,0.75]     Homologous
## 35                  28         -1       1   mono  (0,0.75]     Homologous
## 36                  82         -1       1   mono  (0,0.75]     Homologous
## 37                 113          1       2   poly  (0,0.75]     Homologous
## 38                 140          0       1   mono  (0,0.75]     Homologous
## 39                  70          0       1   mono  (0.75,1]   Heterologous
## 40                  56         -1       1   mono  (-Inf,0]     Homologous
## 41                 140         -1       2   poly  (0,0.75]     Homologous
## 42                  56          1       2   poly  (-Inf,0]     Homologous
## 43                  28          1       2   poly  (0,0.75]     Homologous
## 44                  33          0       2   poly  (0,0.75]     Homologous
## 45                  70         -1       1   mono  (-Inf,0]     Homologous
## 46                  64         -2       2   poly  (0,0.75]     Homologous
## 47                  70         -1       1   mono  (-Inf,0]     Homologous
## 48                  56          0       2   poly  (0.75,1]   Heterologous
## 49                  84          0       2   poly  (0,0.75]     Homologous
## 50                 112          0       2   poly  (0,0.75]     Homologous
## 51                 112         -1       2   poly  (-Inf,0]     Homologous
## 52                  84          0       1   mono  (-Inf,0]     Homologous
## 53                 168          0       1   mono  (-Inf,0]     Homologous
##    .group ibs_range  sampleid1 sampleid2 estimate      p_value CI_lower
## 1       1  0.25-0.5  AR-024-56  AR-024-0    0.000 5.000000e-01    0.000
## 2       1    0-0.25  AR-024-70  AR-024-0    0.000 5.000000e-01    0.000
## 3       1    0-0.25  AR-024-84  AR-024-0    0.000 5.000000e-01    0.000
## 4       1    0-0.25 AR-024-112  AR-024-0    0.000 5.000000e-01    0.000
## 5      15    0.75-1  AR-221-70  AR-221-0    1.000 6.314730e-08    0.732
## 6      33  0.25-0.5 AR-343-112  AR-343-0    0.000 5.000000e-01    0.000
## 7      39  0.25-0.5 AR-366-168  AR-366-0    0.282 1.139043e-02    0.022
## 8      12    0.75-1  AR-187-42  AR-187-0    1.000 1.236722e-06    0.712
## 9      12    0.75-1  AR-187-84  AR-187-0    1.000 7.824760e-08    0.763
## 10     12  0.25-0.5 AR-187-168  AR-187-0    0.175 2.099312e-01    0.000
## 11     41    0-0.25 AR-383-168  AR-383-0    0.000 5.000000e-01    0.000
## 12     23  0.25-0.5  AR-288-70  AR-288-0    0.000 5.000000e-01    0.000
## 13     16    0.75-1 AR-234-112  AR-234-0    0.669 1.240223e-03    0.212
## 14     27  0.5-0.75  AR-306-84  AR-306-0    0.548 6.480622e-03    0.103
## 15     27  0.5-0.75  AR-306-85  AR-306-0    0.567 3.426794e-03    0.138
## 16     25    0.75-1  AR-302-70  AR-302-0    0.560 3.842267e-02    0.000
## 17     32  0.25-0.5  AR-341-84  AR-341-0    0.000 5.000000e-01    0.000
## 18     30    0.75-1 AR-326-168  AR-326-0    0.725 2.205554e-02    0.022
## 19      2    0.75-1  AR-067-42  AR-067-0    1.000 3.856190e-05    0.631
## 20     28  0.25-0.5 AR-322-140  AR-322-0    0.000 5.000000e-01    0.000
## 21     37  0.25-0.5 AR-355-140  AR-355-0    0.031 4.380358e-01    0.000
## 22      3  0.5-0.75 AR-081-140  AR-081-0    0.000 5.000000e-01    0.000
## 23      3  0.5-0.75 AR-081-168  AR-081-0    0.000 5.000000e-01    0.000
## 24     19    0.75-1  AR-248-84  AR-248-0    1.000 5.548439e-06    0.697
## 25     26  0.5-0.75  AR-304-28  AR-304-0    0.000 5.000000e-01    0.000
## 26      9    0.75-1  AR-160-84  AR-160-0    1.000 8.884332e-10    0.775
## 27      5    0.75-1  AR-110-84  AR-110-0    1.000 2.540399e-08    0.768
## 28      6    0.75-1  AR-133-84  AR-133-0    1.000 3.057878e-06    0.662
## 29      6    0.75-1 AR-133-140  AR-133-0    1.000 4.752026e-07    0.718
## 30      7    0.75-1  AR-140-56  AR-140-0    0.683 3.407867e-02    0.000
## 31      7    0.75-1  AR-140-70  AR-140-0    1.000 1.578781e-04    0.585
## 32      8  0.25-0.5  AR-142-42  AR-142-0    0.000 5.000000e-01    0.000
## 33      8  0.25-0.5  AR-142-46  AR-142-0    0.000 5.000000e-01    0.000
## 34      8    0.75-1  AR-142-97  AR-142-0    0.672 9.258504e-04    0.222
## 35     10    0.75-1  AR-173-28  AR-173-0    0.755 3.153185e-03    0.200
## 36     31  0.25-0.5  AR-328-84  AR-328-0    0.111 2.890700e-01    0.000
## 37     31  0.5-0.75 AR-328-112  AR-328-0    0.374 4.673184e-02    0.000
## 38     29    0.75-1 AR-325-140  AR-325-0    0.863 1.458441e-07    0.501
## 39     18    0-0.25  AR-246-70  AR-246-0    0.000 5.000000e-01    0.000
## 40     20    0.75-1  AR-250-56  AR-250-0    1.000 5.050400e-08    0.764
## 41     21    0.75-1 AR-258-140  AR-258-0    0.659 4.000648e-04    0.218
## 42      4    0.75-1  AR-098-56  AR-098-0    1.000 5.196368e-08    0.755
## 43     11  0.25-0.5  AR-176-28  AR-176-0    0.000 5.000000e-01    0.000
## 44     11  0.25-0.5  AR-176-33  AR-176-0    0.000 5.000000e-01    0.000
## 45     13    0.75-1  AR-192-70  AR-192-0    1.000 8.370809e-09    0.764
## 46     14    0.75-1  AR-214-64  AR-214-0    0.672 7.405563e-03    0.089
## 47     17    0.75-1  AR-237-70  AR-237-0    1.000 3.886609e-04    0.536
## 48     22    0-0.25  AR-274-56  AR-274-0    0.000 5.000000e-01    0.000
## 49     24  0.25-0.5  AR-300-84  AR-300-0    0.090 3.160523e-01    0.000
## 50     34  0.25-0.5 AR-345-112  AR-345-0    0.000 5.000000e-01    0.000
## 51     35    0.75-1 AR-346-112  AR-346-0    1.000 1.784710e-06    0.713
## 52     36    0.75-1  AR-348-84  AR-348-0    1.000 2.540399e-08    0.768
## 53     40    0.75-1 AR-382-168  AR-382-0    1.000 7.855351e-07    0.705
##    CI_upper patientid1 patientid2 day_id1 day_id2 comparison_type
## 1     0.341        024        024      56       0          paired
## 2     0.300        024        024      70       0          paired
## 3     0.346        024        024      84       0          paired
## 4     0.251        024        024     112       0          paired
## 5     1.000        221        221      70       0          paired
## 6     0.442        343        343     112       0          paired
## 7     0.635        366        366     168       0          paired
## 8     1.000        187        187      42       0          paired
## 9     1.000        187        187      84       0          paired
## 10    0.656        187        187     168       0          paired
## 11    0.447        383        383     168       0          paired
## 12    0.388        288        288      70       0          paired
## 13    0.938        234        234     112       0          paired
## 14    0.872        306        306      84       0          paired
## 15    0.877        306        306      85       0          paired
## 16    0.963        302        302      70       0          paired
## 17    0.389        341        341      84       0          paired
## 18    0.983        326        326     168       0          paired
## 19    1.000        067        067      42       0          paired
## 20    0.404        322        322     140       0          paired
## 21    0.482        355        355     140       0          paired
## 22    0.570        081        081     140       0          paired
## 23    0.498        081        081     168       0          paired
## 24    1.000        248        248      84       0          paired
## 25    0.474        304        304      28       0          paired
## 26    1.000        160        160      84       0          paired
## 27    1.000        110        110      84       0          paired
## 28    1.000        133        133      84       0          paired
## 29    1.000        133        133     140       0          paired
## 30    0.980        140        140      56       0          paired
## 31    1.000        140        140      70       0          paired
## 32    0.494        142        142      42       0          paired
## 33    0.542        142        142      46       0          paired
## 34    0.939        142        142      97       0          paired
## 35    0.984        173        173      28       0          paired
## 36    0.575        328        328      84       0          paired
## 37    0.807        328        328     112       0          paired
## 38    0.991        325        325     140       0          paired
## 39    0.456        246        246      70       0          paired
## 40    1.000        250        250      56       0          paired
## 41    0.935        258        258     140       0          paired
## 42    1.000        098        098      56       0          paired
## 43    0.489        176        176      28       0          paired
## 44    0.411        176        176      33       0          paired
## 45    1.000        192        192      70       0          paired
## 46    0.978        214        214      64       0          paired
## 47    1.000        237        237      70       0          paired
## 48    0.300        274        274      56       0          paired
## 49    0.547        300        300      84       0          paired
## 50    0.480        345        345     112       0          paired
## 51    1.000        346        346     112       0          paired
## 52    1.000        348        348      84       0          paired
## 53    1.000        382        382     168       0          paired
##                     pair         sig_est ibd_range joint_classification
## 1   AR-024-56 / AR-024-0 not significant    0-0.25          Reinfection
## 2   AR-024-70 / AR-024-0 not significant    0-0.25        Recrudescence
## 3   AR-024-84 / AR-024-0 not significant    0-0.25        Recrudescence
## 4  AR-024-112 / AR-024-0 not significant    0-0.25          Reinfection
## 5   AR-221-70 / AR-221-0     significant    0.75-1              Relapse
## 6  AR-343-112 / AR-343-0 not significant    0-0.25          Reinfection
## 7  AR-366-168 / AR-366-0     significant  0.25-0.5          Reinfection
## 8   AR-187-42 / AR-187-0     significant    0.75-1        Recrudescence
## 9   AR-187-84 / AR-187-0     significant    0.75-1        Recrudescence
## 10 AR-187-168 / AR-187-0 not significant    0-0.25          Reinfection
## 11 AR-383-168 / AR-383-0 not significant    0-0.25          Reinfection
## 12  AR-288-70 / AR-288-0 not significant    0-0.25          Reinfection
## 13 AR-234-112 / AR-234-0     significant  0.5-0.75              Relapse
## 14  AR-306-84 / AR-306-0     significant  0.5-0.75          Reinfection
## 15  AR-306-85 / AR-306-0     significant  0.5-0.75        Recrudescence
## 16  AR-302-70 / AR-302-0     significant  0.5-0.75              Relapse
## 17  AR-341-84 / AR-341-0 not significant    0-0.25          Reinfection
## 18 AR-326-168 / AR-326-0     significant  0.5-0.75              Relapse
## 19  AR-067-42 / AR-067-0     significant    0.75-1              Relapse
## 20 AR-322-140 / AR-322-0 not significant    0-0.25          Reinfection
## 21 AR-355-140 / AR-355-0 not significant    0-0.25          Reinfection
## 22 AR-081-140 / AR-081-0 not significant    0-0.25          Reinfection
## 23 AR-081-168 / AR-081-0 not significant    0-0.25              Relapse
## 24  AR-248-84 / AR-248-0     significant    0.75-1              Relapse
## 25  AR-304-28 / AR-304-0 not significant    0-0.25          Reinfection
## 26  AR-160-84 / AR-160-0     significant    0.75-1        Recrudescence
## 27  AR-110-84 / AR-110-0     significant    0.75-1        Recrudescence
## 28  AR-133-84 / AR-133-0     significant    0.75-1        Recrudescence
## 29 AR-133-140 / AR-133-0     significant    0.75-1              Relapse
## 30  AR-140-56 / AR-140-0     significant  0.5-0.75          Reinfection
## 31  AR-140-70 / AR-140-0     significant    0.75-1              Relapse
## 32  AR-142-42 / AR-142-0 not significant    0-0.25          Reinfection
## 33  AR-142-46 / AR-142-0 not significant    0-0.25              Relapse
## 34  AR-142-97 / AR-142-0     significant  0.5-0.75              Relapse
## 35  AR-173-28 / AR-173-0     significant    0.75-1              Relapse
## 36  AR-328-84 / AR-328-0 not significant    0-0.25          Reinfection
## 37 AR-328-112 / AR-328-0     significant  0.25-0.5          Reinfection
## 38 AR-325-140 / AR-325-0     significant    0.75-1              Relapse
## 39  AR-246-70 / AR-246-0 not significant    0-0.25          Reinfection
## 40  AR-250-56 / AR-250-0     significant    0.75-1        Recrudescence
## 41 AR-258-140 / AR-258-0     significant  0.5-0.75          Reinfection
## 42  AR-098-56 / AR-098-0     significant    0.75-1              Relapse
## 43  AR-176-28 / AR-176-0 not significant    0-0.25          Reinfection
## 44  AR-176-33 / AR-176-0 not significant    0-0.25              Relapse
## 45  AR-192-70 / AR-192-0     significant    0.75-1        Recrudescence
## 46  AR-214-64 / AR-214-0     significant  0.5-0.75              Relapse
## 47  AR-237-70 / AR-237-0     significant    0.75-1        Recrudescence
## 48  AR-274-56 / AR-274-0 not significant    0-0.25          Reinfection
## 49  AR-300-84 / AR-300-0 not significant    0-0.25          Reinfection
## 50 AR-345-112 / AR-345-0 not significant    0-0.25          Reinfection
## 51 AR-346-112 / AR-346-0     significant    0.75-1        Recrudescence
## 52  AR-348-84 / AR-348-0     significant    0.75-1        Recrudescence
## 53 AR-382-168 / AR-382-0     significant    0.75-1        Recrudescence
##    joint_probability
## 1          0.5035808
## 2          0.5035808
## 3          0.5035808
## 4          0.5035808
## 5          0.9999992
## 6          0.9023818
## 7          0.5086858
## 8          0.4787060
## 9          0.4787060
## 10         0.4787060
## 11         0.7329154
## 12         0.9069764
## 13         0.9541120
## 14         0.7117713
## 15         0.7117713
## 16         0.6209101
## 17         0.9428695
## 18         0.8969263
## 19         0.9971211
## 20         0.7992640
## 21         0.8155648
## 22         0.6553398
## 23         0.6553398
## 24         0.9999937
## 25         0.9450287
## 26         0.7485703
## 27         0.7479272
## 28         0.8615093
## 29         0.8615093
## 30         0.6831029
## 31         0.6831029
## 32         0.7883076
## 33         0.7883076
## 34         0.7883076
## 35         0.8595943
## 36         0.5402922
## 37         0.5402922
## 38         0.9999403
## 39         0.7328952
## 40         0.6878086
## 41         0.9371880
## 42         0.9999948
## 43         0.7441066
## 44         0.7441066
## 45         0.6883825
## 46         0.7112593
## 47         0.6339993
## 48         0.9069753
## 49         0.9069767
## 50         0.9049252
## 51         0.8462352
## 52         0.7479272
## 53         0.7451339

Comparison with IBS

classification_summary %>% 
  select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_range = cut(posterior_value, 
                               breaks = c(0, 0.25, 0.5, 0.75, 1), 
                               include.lowest = TRUE, 
                               labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  count(ibs_range, classification, posterior_type, posterior_range)

Plot IBS range vs probabilistic classification (marginal probability)

classification_summary %>% 
  select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_range = cut(posterior_value, 
                               breaks = c(0, 0.25, 0.5, 0.75, 1), 
                               include.lowest = TRUE, 
                               labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%  
  ggplot(aes(x = reorder(pair, episode_number), y = posterior_value, group = episode_number, fill = posterior_type)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_fill_manual(values = c("turquoise3", "magenta3", "skyblue4"),
                       labels = c("posterior_probability_relapse" = "Relapse",
                                  "posterior_probability_recrudescence" = "Recrudescence",
                                  "posterior_probability_reinfection" = "Reinfection")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Sample pair", 
         y     = "Posterior marginal probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~ibs_range, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

### Plot IBS range vs probabilistic classification (joint probability, highest ranking)

classification_summary %>% 
  select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>% 
  ggplot(aes(x = reorder(pair, episode_number), y = joint_probability, group = episode_number, fill = joint_classification)) +
    geom_bar(stat = "identity") +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Sample pair", 
         y     = "Posterior joint probability (highest ranking classification)", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~ibs_range, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Comparison with IBD

classification_summary %>% 
  select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), ibd_range, CI_lower, CI_upper, sig_est) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_range = cut(posterior_value, 
                               breaks = c(0, 0.25, 0.5, 0.75, 1), 
                               include.lowest = TRUE, 
                               labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  count(ibd_range, sig_est, posterior_type, posterior_range)

For reference - all pairwise comparisons

ibd_r_data %>%  
  filter(comparison_type == "paired") %>% 
  ggplot(aes(x = pair, y = estimate, group = patientid1, color = sig_est)) +
    geom_point(size = 3) +
    geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
    scale_color_manual(values = c("not significant" = "darkgrey", 
                                  "significant" = "indianred3")) +
    facet_wrap(~patientid1, scales = "free_x") +
    theme_bw() +    
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Plot IBD estimate and CI for paired samples compared to baseline for each participant

classification_summary %>% 
  ggplot(aes(x = reorder(sampleid1, days_since_enrolment), y = estimate, group = subject_id, color = sig_est)) +
    geom_point(size = 3) +
    geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
    scale_color_manual(values = c("not significant" = "darkgrey", 
                                  "significant" = "indianred3")) +
    labs(x = "Sample",
         y = "IBD",
         fill = "Significance") +
    facet_wrap(~subject_id, scales = "free_x") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

ibd_r_data %>% 
  filter(comparison_type == "not paired") %>% 
  ggplot(aes(x = pair, y = estimate, group = patientid1, color = sig_est)) +
    geom_point(size = 3) +
    geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
    scale_color_manual(values = c("not significant" = "darkgrey", 
                                  "significant" = "indianred3")) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Plot IBD estimate and CI for paired samples compared to baseline for each participant, based on days since enrolment

classification_summary %>% 
  ggplot(aes(x = days_since_enrolment, y = estimate, group = subject_id, color = sig_est)) +
    geom_point(size = 3) +
    geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
    scale_color_manual(values = c("not significant" = "darkgrey", 
                                  "significant" = "indianred3")) +
    labs(x = "Days since last episode",
         y = "IBD",
         fill = "Significance") +
    facet_wrap(~subject_id, scales = "free_x") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Plot IBD range vs probabilistic classification (marginal)

classification_summary %>% 
  select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), ibd_range, CI_lower, CI_upper, sig_est) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_range = cut(posterior_value, 
                               breaks = c(0, 0.25, 0.5, 0.75, 1), 
                               include.lowest = TRUE, 
                               labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%  
  ggplot(aes(x = reorder(pair, episode_number), y = posterior_value, group = episode_number, fill = posterior_type)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_fill_manual(values = c("turquoise3", "magenta3", "skyblue4"),
                       labels = c("posterior_probability_relapse" = "Relapse",
                                  "posterior_probability_recrudescence" = "Recrudescence",
                                  "posterior_probability_reinfection" = "Reinfection")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Sample pair", 
         y     = "Posterior marginal probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~ibd_range, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Plot IBD range vs probabilistic classification (joint probability, highest ranking)

classification_summary %>% 
  select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("joint"), ibd_range, CI_lower, CI_upper, sig_est) %>% 
  ggplot(aes(x = reorder(pair, episode_number), y = joint_probability, group = episode_number, fill = joint_classification)) +
    geom_bar(stat = "identity") +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Sample pair", 
         y     = "Posterior joint probability (highest ranking classification)", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~ibd_range, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Confusion matrices: PNH vs probabilistic classification

Classification by 1-PNH (IBS) range

confusion_data_ibsrange <- classification_summary %>% 
  select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, 
         # renaming for clarity
         pnh_classification = classification) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "marginal_classification", 
               values_to = "marginal_probability") %>% 
  mutate(marginal_classification = case_when(marginal_classification == "posterior_probability_recrudescence" ~ "Recrudescence",
                                             marginal_classification == "posterior_probability_relapse" ~ "Relapse",
                                             marginal_classification == "posterior_probability_reinfection" ~ "Reinfection"),
         marginal_classification = factor(marginal_classification, 
                                          levels = c("Relapse", "Recrudescence", "Reinfection")),
         marginal_probability_range = cut(marginal_probability, 
                                          breaks = c(0, 0.25, 0.5, 0.75, 1), 
                                          include.lowest = TRUE, 
                                          labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
         joint_probability_range = cut(joint_probability, 
                                       breaks = c(0, 0.25, 0.5, 0.75, 1), 
                                       include.lowest = TRUE, 
                                       labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  select(subject_id, episode_number, days_since_enrolment, ibs_range, pnh_classification,  marginal_classification, marginal_probability, marginal_probability_range, joint_classification, joint_probability, joint_probability_range) %>% 
  # now only keep the highest 'ranking' marginal classification
  group_by(subject_id, episode_number) %>% 
  slice_max(order_by = marginal_probability, n = 1, with_ties = F) %>% 
  ungroup() %>% 
  group_by(ibs_range, marginal_classification, joint_classification) %>%
  tally() %>%
  dcast(ibs_range ~ marginal_classification + joint_classification, value.var = "n", fill = 0)
confusion_data_ibsrange %>% 
  melt(id.vars = "ibs_range") %>% 
  ggplot(aes(ibs_range, variable, fill = value)) +
    geom_tile() +
    scale_fill_gradient(low = "white", high = "red") +
    geom_text(aes(label = value)) +
    theme_minimal() +
    labs(x = "IBS range (1-PNH)",
         y = "Marginal vs joint classification",
         fill = "Count")

Classification comparison

# Create a confusion matrix for each pair
confusion_data_pnhstate <- 
  classification_summary %>% 
  select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, 
         # renaming for clarity
         pnh_classification = classification) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "marginal_classification", 
               values_to = "marginal_probability") %>% 
  mutate(marginal_classification = case_when(marginal_classification == "posterior_probability_recrudescence" ~ "Recrudescence",
                                             marginal_classification == "posterior_probability_relapse" ~ "Relapse",
                                             marginal_classification == "posterior_probability_reinfection" ~ "Reinfection"),
         marginal_classification = factor(marginal_classification, 
                                          levels = c("Relapse", "Recrudescence", "Reinfection")),
         marginal_probability_range = cut(marginal_probability, 
                                          breaks = c(0, 0.25, 0.5, 0.75, 1), 
                                          include.lowest = TRUE, 
                                          labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
         joint_probability_range = cut(joint_probability, 
                                       breaks = c(0, 0.25, 0.5, 0.75, 1), 
                                       include.lowest = TRUE, 
                                       labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
         # pnh_state = case_when(pnh_classification == "Heterologous" ~ "Reinfection",
         #                       pnh_classification == "Homologous" ~ "Relapse or Recrudescence")
         pnh_state = case_when(ibs_range == "0-0.25" | ibs_range == "0.25-0.5" ~ "Reinfection",
                               (ibs_range == "0.5-0.75" | ibs_range == "0.75-1") ~ "Relapse or Recrudescence")
         ) %>% 
  select(subject_id, episode_number, days_since_enrolment, ibs_range, pnh_classification, pnh_state, marginal_classification, marginal_probability, marginal_probability_range, joint_classification, joint_probability, joint_probability_range) %>% 
  # now only keep the highest 'ranking' marginal classification
  group_by(subject_id, episode_number) %>% 
  slice_max(order_by = marginal_probability, n = 1, with_ties = F) %>% 
  ungroup() %>% 
  group_by(pnh_state, marginal_classification, joint_classification) %>%
  tally() %>%
  dcast(pnh_state ~ marginal_classification + joint_classification, value.var = "n", fill = 0)
confusion_data_pnhstate %>% 
  melt(id.vars = "pnh_state") %>% 
  ggplot(aes(pnh_state, variable, fill = value)) +
    geom_tile() +
    scale_fill_gradient(low = "white", high = "red") +
    geom_text(aes(label = value)) +
    theme_minimal() +
    labs(x = "PNH classification",
         y = "Marginal vs joint classification",
         fill = "Count")

---
title: "Running Pv3Rs for Solomon Islands data"
author: "Shazia Ruybal-Pesántez"
date: "`r Sys.Date()`"
output: 
  html_document:
    toc: true
    toc_float: true
    code_folding: hide 
    code_download: true
    fig_width: 8
    fig_height: 6
    theme: cosmo
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(janitor)
library(here)
library(patchwork)
library(RColorBrewer)
library(reshape2)

# install.packages('Pv3Rs', repos = c('https://plasmogenepi.r-universe.dev', 'https://cloud.r-project.org'))

# devtools::install_github("aimeertaylor/Pv3Rs")
# Pv3Rs downloaded from Github on 7 Aug 2024
library(Pv3Rs)
```

## Set global variables
From TO/JS - This section sets some flags that can be tweaked to re-run the analysis with different filtering parameters:

`RUN_EXAMPLE`: allows discarding alleles that have a frequency below a threshold (defaults to FALSE),
`WITHIN_INDIVIDUAL_ALLELE_FREQ_THR`: allows discarding alleles that have a frequency below a threshold (defaults to 0),
`BENCHMARK_MARKERS`: list of markers to include in the analysis (defaults to the full list established by Jason, could be reduced down to 3 for memory optimization),
`MAX_MOI_TO_INCLUDE`: allows discarding participant data when they exhibit very complex infection patterns, counted as the total number of clones across all infection events (defaults to 8),
`PRIOR_3RS`: a named vector with prior probabilities of recrudescence (C), relapse (L) or reinfection (I) (defaults to uniform, i.e. 1/3 each, but tweaked to minimize recrudescence in the present study).

```{r global vars}
## RUN_EXAMPLE
##   Boolean flag to enable/disable running the example Pv3Rs posterior 
##   computation on a single episode sampled at random from the dataset.
## DEFAULT: FALSE
RUN_EXAMPLE <- TRUE

## WITHIN_INDIVIDUAL_ALLELE_FREQ_THR
##   Minimum threshold above which an allele is preserved in individual
##   haplotype data to be preserved when reconstructing infection 
##   history. Note that this will *not* discard these alleles from the
##   population-level allele frequency that is derived from the initial
##   visit, but only discard that allele from individual observations 
##   when it is 'too rare to be exploited'.
## DEFAULT: 0
WITHIN_INDIVIDUAL_ALLELE_FREQ_THR <- 0

## BENCHMARK_MARKERS
##   Vector of character string with names matching those from the 
##   markers of interest for amplicon sequencing. Only the markers 
##   listed in that vector will be preserved in individual-level data
##   and identity of infection will be solely based on the alleles 
##   observed for these markers.
##   The markers are listed by importance as discovered by Jason.
## DEFAULT: all markers (could be suboptimal/too memory-consuming?)
BENCHMARK_MARKERS <- c(
  "Chr05",
  "Chr07", 
  "Chr09", 
  "Chr10",
  "Chr08",
  "Chr13",
  "Chr11",
  "Chr03",
  "Chr01",
  "Chr02",
  "Chr14"
)

## MAX_MOI_TO_INCLUDE
##   As per Aimee's guidelines: 
##     We do not recommend running compute_posterior() for data 
##     whose total genotype count exceeds eight, where the total 
##     genotype count is the sum of per-episode maximum per-marker 
##     allele counts.
##   The MAX_MOI_TO_INCLUDE expects an integer that will discard all
##   individuals having a summed MOI > 8 across all recorded episodes.
## DEFAULT: 8
MAX_MOI_TO_INCLUDE <- 8

## PRIORS_3RS
##   A vector of probabilities, summing to 1, corresponding to 
##   the probability of each stage for the 3Rs for Pv episodes.
##   The vector order is re(C)rudescence, re(L)apse, re(I)nfection.
##   In this clinical trial, we assume recrudescence is possible so, 
##   we use the default priors 
## DEFAULT: c("C" = 1/3, "L" = 1/3, "I" = 1/3)
PRIOR_3RS <- c("C" = 1/3, "L" = 1/3, "I" = 1/3)
# Note: the below probabilities were used for SeroTAT study because it does not involve treatment at baseline
# PRIOR_3RS <- c("C" = 0.10, "L" = 0.45, "I" = 0.45)
```

## Solomon Islands data curation

Read in data from Solomon Islands:
```{r load data}
sols <- read.csv(here("data/final", "sols_raw_data.csv"))

# head(sols)
# names(sols)
```

### Number of unique haplotypes (ie marker cardinality):
```{r unique haps}
sols %>% 
  group_by(marker_id) %>% 
  summarise(unique_haplotypes = n_distinct(Haplotype))
```

### Counts of each haplotype:
```{r hap counts}
sols %>% count(marker_id, Haplotype)
```

### "MOI":
```{r moi}
sols %>% 
  group_by(sample, marker_id) %>%
  summarise(n_haplotypes = n(), .groups = 'drop') %>%
  group_by(sample) %>% 
  summarise(max_moi = max(n_haplotypes)) 
```

### Recurrences
Participants with more than one episode for inference (n=41 participants, n=99 Pv isolates):
```{r recurrences}
sols %>% 
  select(info, episodes) %>% 
  distinct() %>% 
  arrange(info) %>% 
  filter(episodes>1) 

ids_recurrent <- sols %>% clean_names() %>% filter(episodes > 1) %>% distinct(info)
sols_recurrent <- sols %>% clean_names() %>%filter(episodes > 1)

# setdiff(ids_recurrent$info, sols_recurrent$info)
# setdiff(sols_recurrent$info, ids_recurrent$info)

# sols_recurrent %>% distinct(sample) %>% arrange(sample) # 99 isolates
```

```{r plot recurrences, fig.height=8, fig.width=8}
sols_recurrent %>% 
  distinct(sample) %>% 
  separate(sample, into = c("sample", "episode_day"), sep = "-(?=[0-9]+$)") %>% 
  ggplot(aes(x = episode_day, y = sample)) +
    geom_line(aes(group = sample), color = "darkgrey") +
    geom_point() +
    theme_bw()
```

### Data on episode number and time since last episode
```{r episode summary}
episode_summary <- sols_recurrent %>% 
  distinct(info, info_d, vis_date, sample) %>% 
  mutate(vis_date = mdy(vis_date)) %>%
  arrange(info, vis_date) %>% 
  group_by(info) %>% 
  mutate(
    # get the episode number
    episode_number = row_number(),
    # calculate days since enrolment, just a check with dates
    days_since_enrolment = as.integer(vis_date - min(vis_date[info_d == 0])),
    # calculate days since last episode using lag() and making enrolment episodes 0 days since last
    days_since_last_episode = replace_na(as.integer(vis_date - lag(vis_date)), 0)
  ) %>% 
  ungroup()
```


## Haplotype frequencies
We want to remove any haplotypes that appear only once at very low frequencies. Haplotypes should already be filtered to be observed in at least 2 samples and within-host frequency >=1% (in full dataset).

```{r singletons}
singleton_haps <- sols_recurrent %>% 
  select(sample, marker_id, haplotype, frequency, count) %>% 
  count(haplotype) %>% 
  arrange(n) %>% 
  filter(n==1) %>% 
  pull(haplotype)

sols_recurrent %>% 
  filter(haplotype %in% singleton_haps) %>% 
  ggplot(aes(x = haplotype, y = count)) +
    geom_hline(yintercept = 100, linetype = "dashed") +
    geom_point(aes(color = frequency, shape = follow_x), size = 3) +
    labs(x = "singleton haplotype",
         y = "read count",
         color = "within-sample frequency",
         shape = "timepoint") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```
## Haplotypes for inference
As per Aimee, to avoid bias due to within-host selection of recrudescent parasites, we use only enrolment episodes to estimate population-level alelle frequencies. So, we will filter out any haplotypes that do not appear at baseline (drop n=8 haplotypes). Details below on their within-sample frequencies and read counts.
```{r haps for inference}
# n=569 haps
baseline_haps <- sols_recurrent %>% 
  filter(follow_x == "baseline") %>% 
  pull(haplotype)

sols_recurrent %>% 
  filter(!haplotype %in% baseline_haps) %>% 
  select(sample, marker_id, haplotype, frequency, count) %>% 
  arrange(frequency, count)
```

## Final data for inference
```{r final analysis data}
analysis_data <- sols_recurrent %>%
  select(subject_id = info, 
         sample_id = sample, 
         treatment_arm = trt_label, 
         days_since_treatment = info_d,
         timepoint = follow_x,
         visit_date = vis_date,
         age_years = age_y, 
         sex = gender, 
         episodes, 
         marker_id, 
         haplotype, 
         type,
         frequency,
         mean_moi,
         max_moi) %>% 
  # add extra epi info on episode number and time since last episode
  left_join(episode_summary %>% select(subject_id = info, 
                                       sample_id = sample, 
                                       episode_number,
                                       days_since_enrolment,
                                       days_since_last_episode),
            by = c("subject_id", "sample_id"))  %>% 
  # keep only haplotypes present at baseline
  filter(haplotype %in% baseline_haps) %>% 
  # ensure dates are date class
  mutate(visit_date = mdy(visit_date)) 
```

### Baseline allele frequencies
```{r test allele freqs, eval=F}
test_df <- analysis_data %>% 
  # Use only baseline samples
  filter(timepoint == "baseline") %>% 
  # filter data frame by marker
  filter(marker_id == "Chr11")

test_df %>% 
      mutate(haplotype = factor(haplotype)) %>% 
      select(sample_id, haplotype, frequency) %>% 
      pivot_wider(names_from = haplotype, 
                  values_from = frequency, 
                  values_fill = 0) %>% 
      pivot_longer(cols = -sample_id, 
                   names_to = "haplotype", 
                   values_to = "frequency") %>% 
      # Get population-level haplotype frequency, 
      # correcting for when within-individual sum is not equal 
      # to 1, as can happen when a minority clone is <2% 
      group_by(sample_id) %>% 
      mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>% 
  
      # get population-level mean freq
      group_by(haplotype) %>% 
      summarise(poplevel_mean_freq = mean(frequency, na.rm = TRUE)) %>% 
      adorn_totals("row")
```

```{r baseline allele freqs}
## Derive baseline allele frequency - This is modified from Thomas/Jason script for our data
baseline_fs <- analysis_data %>% 
  
  # Use only baseline samples
  filter(timepoint == "baseline") %>% 
  
  # split data frame by marker
  group_by(marker_id) %>% 
  group_split() %>% 
  
  # Derive a within-marker list of frequencies, by individual
  lapply(function(x) {
    x <- x %>% 
      # Build a within-individual frequency table that 
      # always includes every haplotype (even the ones absent)
      mutate(haplotype = factor(haplotype)) %>% 
      select(sample_id, haplotype, frequency) %>% 
      pivot_wider(names_from = haplotype, 
                  values_from = frequency, 
                  values_fill = 0) %>% 
      pivot_longer(cols = -sample_id, 
                   names_to = "haplotype", 
                   values_to = "frequency") %>% 
      # Get population-level haplotype frequency, 
      # correcting for when within-individual sum is not equal 
      # to 1, as can happen when a minority clone is <2%
      group_by(sample_id) %>% 
      mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>% 
      group_by(haplotype) %>% 
      summarise(frequency_pop_mean = mean(frequency, na.rm = TRUE))
    
    return(deframe(x))
  }) %>% 
  # get marker_id from group_keys from the group dfs 
  setNames(nm = analysis_data %>% group_by(marker_id) %>% group_keys() %>% pull(marker_id))

# Here we can also save as dataframe for easier printing and table-ready for paper
baseline_fs_df <- analysis_data %>% 
  # Use only baseline samples
  filter(timepoint == "baseline") %>% 
  
  # Group by marker_id and sample_id for further calculations
  group_by(marker_id, sample_id) %>% 
  
  # Build a within-individual frequency table that always includes every haplotype (even the ones absent)
  mutate(haplotype = factor(haplotype)) %>% 
  select(marker_id, sample_id, haplotype, frequency) %>% 
  pivot_wider(names_from = haplotype, values_from = frequency, values_fill = list(frequency = 0)) %>% 
  pivot_longer(cols = -c(marker_id, sample_id), names_to = "haplotype", values_to = "frequency") %>% 
  
  # Get population-level haplotype frequency, correcting for when within-individual sum is not equal to 1
  group_by(marker_id, sample_id) %>% 
  mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>% 
  group_by(marker_id, haplotype) %>% 
  summarise(frequency_pop_mean = mean(frequency, na.rm = TRUE), .groups = 'drop') %>% 
  
  # Ensure haplotypes are correctly associated with their marker_id - note that this works for us because , in future would have to make this flexible to allow for haplotype names that are not reliant on having marker_id
  filter(str_detect(haplotype, marker_id))

baseline_fs_df
```

## Pv3Rs
### Example on one participant: AR-067
```{r pv3rs one example}
# This has been modified from Thomas/Jason script 

## Pick an individual at random to run Pv3Rs
# indiv_name <- sample(unique(analysis_data$subject_id), size = 1)
indiv_name <- "AR-067"

## Prepare the data
# 1- Subset haplotype data to specific individual and apply filters
indiv_haplotype_data <- analysis_data %>% 
  # Restrict to a single patient
  filter(subject_id == indiv_name) %>% 
  # Restrict to a subset of markers, if needed
  filter(marker_id %in% BENCHMARK_MARKERS) %>% 
  # Restrict to summed MOI below threshold
  group_by(subject_id, episode_number, marker_id) %>% 
  mutate(MOI_per_marker = sum(n())) %>% 
  group_by(subject_id, episode_number) %>% 
  mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))

# 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
indiv_MOI <- indiv_haplotype_data %>% 
  select(subject_id, episode_number, 
         marker_id, starts_with("MOI_")) %>% 
  distinct() %>% 
  group_by(subject_id, episode_number) %>% 
  # Get highest per-marker MOI only for each episode
  # (drop marker_id in case of ties with highest per-marker MOI)
  select(-marker_id) %>% 
  distinct() %>% 
  filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))
```

Prepare test data
```{r}
# Finish data preparation
  indiv_haplotype_data <- indiv_haplotype_data %>% 
    group_by(episode_number) %>% 
    group_split() %>% 
    lapply(function(x) {
      res <- x %>% 
        select(sample_id, episode_number, 
               marker_id, haplotype, frequency) %>% 
        # For sensitivity analysis, allow to include/drop allele 
        # based on their within-individual frequency
        filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>% 
        select(-sample_id, -episode_number, -frequency) %>% 
        distinct() %>% 
        # Prevent dropping of markers that are not characterised 
        # by setting .drop to FALSE
        group_by(marker_id, .drop = FALSE) %>% 
        group_split() %>% 
        lapply(function(y) {
          unique(y$haplotype)
        })
      
      # Returned a list named with each episode, 
      # setting marker allele to NA in case none are observed
      return(lapply(setNames(res, BENCHMARK_MARKERS), 
                    function(y) {
                      if (length(y) == 0) return(NA) else return(y)
                    }))
    })
```

```{r run compute posterior one indiv}
# Run Aimee's posterior estimation
indiv_posterior <- Pv3Rs::compute_posterior(y  = indiv_haplotype_data, 
                                            fs = baseline_fs[BENCHMARK_MARKERS])

indiv_posterior
```

The joint posterior probability of relapse is 99.7%, when we look at the genetic data, this is in line with the inferred probability of relpase. 
```{r plot haps}
sols_recurrent %>% 
  filter(info == "AR-067") %>% 
  ggplot(aes(x = haplotype, y = factor(info_d), fill = factor(haplotype))) +
  geom_tile() +
    facet_grid(~marker_id, scales = "free_x") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```
### Run Pv3Rs one random individual
```{r subset haplotype data at random}
# This has been modified from Thomas/Jason script 

## Pick an individual at random to run Pv3Rs
indiv_name <- sample(unique(analysis_data$subject_id), size = 1)

## Prepare the data
# 1- Subset haplotype data to specific individual and apply filters
indiv_haplotype_data <- analysis_data %>% 
  # Restrict to a single patient
  filter(subject_id == indiv_name) %>% 
  # Restrict to a subset of markers, if needed
  filter(marker_id %in% BENCHMARK_MARKERS) %>% 
  # Restrict to summed MOI below threshold
  group_by(subject_id, episode_number, marker_id) %>% 
  mutate(MOI_per_marker = sum(n())) %>% 
  group_by(subject_id, episode_number) %>% 
  mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))

# 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
indiv_MOI <- indiv_haplotype_data %>% 
  select(subject_id, episode_number, 
         marker_id, starts_with("MOI_")) %>% 
  distinct() %>% 
  group_by(subject_id, episode_number) %>% 
  # Get highest per-marker MOI only for each episode
  # (drop marker_id in case of ties with highest per-marker MOI)
  select(-marker_id) %>% 
  distinct() %>% 
  filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))
```


Now that the data has been subset, Pv3Rs::compute_posterior() will be called (only if the participant meets eligibility criteria as defined by the global variables set earlier in this report).
```{r run Pv3Rs one random indiv, eval=F}
if (RUN_EXAMPLE & unique(indiv_MOI$MOI_summed) <= MAX_MOI_TO_INCLUDE) {
  # Verbose
  cat("Running Pv3Rs for: ", 
      indiv_name, 
      " (summed MOI = ", 
      unique(indiv_MOI$MOI_summed), 
      ").\n", 
      sep = "")
  
  # Finish data preparation
  indiv_haplotype_data <- indiv_haplotype_data %>% 
    group_by(episode_number) %>% 
    group_split() %>% 
    lapply(function(x) {
      res <- x %>% 
        select(sample_id, episode_number, 
               marker_id, haplotype, frequency) %>% 
        # For sensitivity analysis, allow to include/drop allele 
        # based on their within-individual frequency
        filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>% 
        select(-sample_id, -episode_number, -frequency) %>% 
        distinct() %>% 
        # Prevent dropping of markers that are not characterised 
        # by setting .drop to FALSE
        group_by(marker_id, .drop = FALSE) %>% 
        group_split() %>% 
        lapply(function(y) {
          unique(y$haplotype)
        })
      
      # Returned a list named with each episode, 
      # setting marker allele to NA in case none are observed
      return(lapply(setNames(res, BENCHMARK_MARKERS), 
                    function(y) {
                      if (length(y) == 0) return(NA) else return(y)
                    }))
    })
  
  # Run Aimee's posterior estimation
  indiv_posterior <- Pv3Rs::compute_posterior(y  = indiv_haplotype_data, 
                                              fs = baseline_fs[BENCHMARK_MARKERS])
} else {
  # Verbose
  cat("NOT Running Pv3Rs for: ", 
      indiv_name, 
      " because either summed MOI exceeds threshold (observed = ", 
      unique(indiv_MOI$MOI_summed), 
      ", MAX_MOI_TO_INCLUDE = ", 
      MAX_MOI_TO_INCLUDE, 
      "), or RUN_EXAMPLE was set to FALSE.\n", 
      sep = "")
  
  # Return NULL
  indiv_posterior <- NULL
}

indiv_posterior
```

```{r plot haps for indiv, eval=F}
sols_recurrent %>% 
  filter(info == indiv_name) %>% 
  ggplot(aes(x = haplotype, y = factor(info_d), fill = factor(haplotype))) +
  geom_tile() +
    facet_grid(~marker_id, scales = "free_x") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```
### Run for all participants
```{r run pv3rs all}
# Start timer
t_start <- Sys.time()

# Initialize an empty list to store the results
indiv_posteriors <- list()

# Loop through each unique individual name
for (indiv_name in unique(analysis_data$subject_id)) {
  # Verbose
  cat("ID : ", indiv_name, "...\n", sep = "")
  
  ## Prepare the data
  # 1- Subset haplotype data to specific individual and apply filters
  indiv_haplotype_data <- analysis_data %>% 
    # Restrict to a single patient
    filter(subject_id == indiv_name) %>% 
    # Ensure episodes are in order
    arrange(episode_number) %>%  
    # Restrict to a subset of markers
    filter(marker_id %in% BENCHMARK_MARKERS) %>% 
    # Restrict to summed MOI below threshold
    group_by(subject_id, episode_number, marker_id) %>% 
    mutate(MOI_per_marker = sum(n())) %>% 
    group_by(subject_id, episode_number) %>% 
    mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>% 
    ungroup() %>% 
    mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))
  
  # 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
  indiv_MOI <- indiv_haplotype_data %>% 
    select(subject_id, episode_number, 
           marker_id, starts_with("MOI_")) %>% 
    distinct() %>% 
    group_by(subject_id, episode_number) %>% 
    # Get highest per-marker MOI only for each episode
    # (drop marker_id in case of ties with highest per-marker MOI)
    select(-marker_id) %>% 
    distinct() %>% 
    filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>% 
    ungroup() %>% 
    mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))
  
  ## Run Pv3Rs only if MOI below threshold         
  if (unique(indiv_MOI$MOI_summed) <= MAX_MOI_TO_INCLUDE) {
    # Verbose
    cat("Running Pv3Rs for: ", 
        indiv_name, 
        " (summed MOI = ", 
        unique(indiv_MOI$MOI_summed), 
        ").\n", 
        sep = "")
    
    # Preserve episode numbers for naming the output list
    indiv_episode <- unique(indiv_haplotype_data$episode_number)
    cat("The episode number is: ", indiv_episode) # printing to check episode order number is correct
    
    # Finish data preparation
    indiv_haplotype_data <- indiv_haplotype_data %>% 
      group_by(episode_number) %>% 
      group_split() %>% 
      lapply(function(x) {
        res <- x %>% 
          select(sample_id, episode_number, 
                 marker_id, haplotype, frequency) %>% 
          # For sensitivity analysis, allow to include/drop allele 
          # based on their within-individual frequency
          filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>% 
          select(-sample_id, -episode_number, -frequency) %>% 
          distinct() %>% 
          # Prevent dropping of markers that are not characterized 
          # by setting .drop to FALSE
          group_by(marker_id, .drop = FALSE) %>% 
          group_split() %>% 
          lapply(function(y) {
            unique(y$haplotype)
          })
        
        # Returned a list named with each episode, 
        # setting marker allele to NA in case none are observed
        return(lapply(setNames(res, BENCHMARK_MARKERS), 
                      function(y) {
                        if (length(y) == 0) return(NA) else return(y)
                      }))
      })
    
    # Run Aimee's posterior estimation
    indiv_posterior <- compute_posterior(y     = indiv_haplotype_data,
                                         fs    = baseline_fs[BENCHMARK_MARKERS], 
                                         prior = matrix(PRIOR_3RS, 
                                                        nrow     = length(indiv_haplotype_data), 
                                                        ncol     = length(PRIOR_3RS), 
                                                        byrow    = TRUE, 
                                                        dimnames = list(c(1:length(indiv_haplotype_data)), 
                                                                        names(PRIOR_3RS))))
    
  } else {
    # Verbose
    cat("NOT Running Pv3Rs for: ", 
        indiv_name, 
        " because summed MOI exceeds threshold (observed = ", 
        unique(indiv_MOI$MOI_summed), 
        ", MAX_MOI_TO_INCLUDE = ", 
        MAX_MOI_TO_INCLUDE, 
        ").\n", 
        sep = "")
    
    # Return NULL
    indiv_episode    <- unique(indiv_haplotype_data$episode_number)
    indiv_posterior <- NULL
  }
  
  # Append the results to the list
  indiv_posteriors[[indiv_name]] <- list("subject_id" = indiv_name, 
                                         "episode_number"       = indiv_episode, 
                                         "Pv3Rs"       = indiv_posterior)
}

# End timer
t_end <- Sys.time()
cat("Pv3Rs for the whole dataset took : ", as.numeric(difftime(time1 = t_end, 
                                                               time2 = t_start, 
                                                               units = "secs"))/60, " mins", "\n", sep = "")

# Present the marginal data in a clearer format
indiv_posteriors_marginal <- do.call(rbind, 
                                     lapply(indiv_posteriors, function(x) {
                                       if (!is.null(x[["Pv3Rs"]])) {
                                         return(data.frame("subject_id"               = x[["subject_id"]], 
                                                           "episode_number"                     = x[["episode_number"]][-1], 
                                                           "Posterior_marginal_prob_C" = x[["Pv3Rs"]]$marg[, "C"], 
                                                           "Posterior_marginal_prob_L" = x[["Pv3Rs"]]$marg[, "L"], 
                                                           "Posterior_marginal_prob_I" = x[["Pv3Rs"]]$marg[, "I"]))
                                       } else {
                                         return(NULL)
                                       }
                                       
                                     }))
row.names(indiv_posteriors_marginal) <- 1:nrow(indiv_posteriors_marginal)

# Present the joint posterior estimates in a clearer format
indiv_posteriors_joint <- do.call(rbind, 
                                  lapply(indiv_posteriors, function(x) {
                                    if (!is.null(x[["Pv3Rs"]])) {
                                      joint_probs <- x[["Pv3Rs"]]$joint
                                      # Extract the state pairs and probabilities
                                      state_pairs <- names(joint_probs)
                                      prob_values <- as.numeric(joint_probs)
                                      
                                      # Create a data frame with subject_id, episode_number, and joint probabilities
                                      return(data.frame("subject_id"      = rep(x[["subject_id"]], length(state_pairs)), 
                                                        "episode_number"  = rep(x[["episode_number"]][-1], each = length(state_pairs)),
                                                        "state_pair"      = state_pairs, 
                                                        "joint_probability" = prob_values))
                                    } else {
                                      return(NULL)
                                    }
                                  }))
row.names(indiv_posteriors_joint) <- 1:nrow(indiv_posteriors_joint)

# Save Pv3Rs output because it's time consuming and 
# we don't want to re-run it every time.
save(list = c("RUN_EXAMPLE", "WITHIN_INDIVIDUAL_ALLELE_FREQ_THR", "BENCHMARK_MARKERS", "MAX_MOI_TO_INCLUDE", "PRIOR_3RS", 
              "analysis_data", "baseline_fs", 
              "indiv_posteriors", "indiv_posteriors_marginal", "indiv_posteriors_joint"), 
     file = paste0("./outputs/Pv3Rs_sols_posteriors_", 
                   strftime(Sys.time(), format = "%Y%m%d_%H%M%S"), 
                   ".RData"))
```

### Explore the posterior
The marginal probabilities give us the probability of the three states for each recurrent episode. However, this does not consider the joint probability of different states when a person experienced more than one recurrent episode. 
```{r marginal probs}
indiv_posteriors_marginal
```

The joint probabilites give us values for each possible combination of states, depending on the number of recurrent episodes experienced by the participant. 
```{r joint probs}
joint_summary <- indiv_posteriors_joint %>% 
                    group_by(subject_id, state_pair, joint_probability) %>% 
                    filter(episode_number == max(episode_number)) %>% 
                    mutate(percentage = round(joint_probability*100, 3),
                           total_recurrences = episode_number-1) %>%
                    select(-episode_number) %>% 
                    arrange(subject_id, total_recurrences, percentage) %>% 
                    relocate(total_recurrences, .before = state_pair)

joint_summary
```

## Plot marginal probabilities 
```{r marg probs}
marginal_summary <- indiv_posteriors_marginal %>% 
  pivot_longer(cols = !subject_id & !episode_number, 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_classification = case_when(posterior_type == "Posterior_marginal_prob_C" ~ "Recrudescence",
                                              posterior_type == "Posterior_marginal_prob_L" ~ "Relapse",
                                              posterior_type == "Posterior_marginal_prob_I" ~ "Reinfection"),
         posterior_classification = factor(posterior_classification, 
                                           levels = c("Relapse", "Recrudescence", "Reinfection"))) %>% 
  select(-posterior_type)

marginal_summary
```

### By episode number
```{r plot probabilities, fig.height=8, fig.width=8}
marginal_summary %>% 
  group_by(subject_id) %>%
  arrange(desc(posterior_value), .by_group = TRUE) %>%
  mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>%
  
  ggplot(aes(x = factor(episode_number), y = posterior_value, group = episode_number, fill = posterior_classification)) + 
    geom_bar(stat = "identity", position = "fill") +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Episode number", 
         y     = "Posterior probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~subject_id)
```

### By treatment arm
```{r plot probabilites by treatment arm, fig.height=8, fig.width=8}
marginal_summary %>% 
  left_join(analysis_data %>% select(subject_id, treatment_arm) %>% distinct(), 
            by = "subject_id") %>% 
  group_by(subject_id) %>% 
  arrange(desc(posterior_value), .by_group = TRUE) %>% 
  mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>% 
  ggplot(aes(x = factor(episode_number), y = posterior_value, group = episode_number, fill = posterior_classification)) + 
    geom_bar(stat = "identity", position = "fill") +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
  
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Episode number", 
         y     = "Posterior probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(treatment_arm ~ subject_id)
```

### By days since last episode 
```{r plot posterior probabilities with time, fig.height=8, fig.width=8}
marginal_summary %>% 
  left_join(analysis_data %>% select(subject_id, episode_number, treatment_arm, visit_date, days_since_enrolment, days_since_last_episode) %>% distinct(), 
            by = c("subject_id", "episode_number")) %>% 
  group_by(subject_id) %>%
  arrange(desc(posterior_value), .by_group = TRUE) %>%
  mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>%
  ggplot(aes(x = days_since_enrolment, y = posterior_value, group = episode_number, fill = posterior_classification)) + 
  geom_bar(stat = "identity", position = "fill", width = 3) +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
  
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
  labs(x     = "Days since enrolment", 
       y     = "Posterior probability", 
       fill = "") + 
  theme_bw() +
  facet_wrap(~subject_id)
```

## Plot joint probabilities
```{r color palettes}
pal1 <- c(
  "C" = "turquoise3", 
  "I" = "magenta3", 
  "L" = "skyblue4")

pal2 <- c(
  "II" = "magenta3",
  "IL" = "darkorange2",
  "LI" = "goldenrod2",
  "LL" = "skyblue4",
  "CI" = "saddlebrown",
  "CL" = "turquoise4",
  "IC" = "darkolivegreen",
  "LC" = "lightseagreen"
)

pal3 <- c(
  "IIL" = "darkorange3",
  "ILL" = "orangered3",
  "LIL" = "goldenrod3",
  "LLL" = "skyblue4",
  "CCI" = "sienna4",
  "CCL" = "cadetblue3",
  "CLI" = "darkcyan",
  "CLL" = "steelblue3",
  "LCI" = "peru",
  "LCL" = "powderblue",
  "LLI" = "orange3"
)

pal4 <- c(
  "ICCI" = "saddlebrown",
  "ICCL" = "darkseagreen",
  "ICLI" = "orange3",
  "ICLL" = "lightsteelblue",
  "ILCI" = "darkkhaki",
  "ILCL" = "skyblue3",
  "ILLI" = "gold3",
  "LCCI" = "rosybrown",
  "LCCL" = "lightblue3",
  "LCLI" = "goldenrod3",
  "LCLL" = "deepskyblue3",
  "LLCI" = "tan",
  "LLCL" = "mediumturquoise",
  "LLLI" = "darkorange3"
)

combined_pal <- c(pal1, pal2, pal3, pal4)
```

```{r plotting joint fxn}
plotJointProb <- function(data, recurrence_n, prob_threshold, color_palette, ...){
  data %>% 
    filter(total_recurrences == recurrence_n, joint_probability > prob_threshold) %>% 
    group_by(subject_id) %>% 
    mutate(state_pair = fct_reorder(state_pair, joint_probability)) %>%
    ungroup() %>% 
    ggplot(aes(x = joint_probability, 
               y = subject_id, 
               fill = state_pair)) +
      geom_bar(position = "stack", stat = "identity") +
      scale_x_continuous(expand = c(0, 0)) +
      scale_y_discrete(expand = c(0, 0)) +
      scale_fill_manual(values = color_palette) +
      labs(x = "Joint probability estimate",
           y = "Participant",
           fill = "Classification state") +
      theme_bw() + 
    facet_wrap(~total_recurrences, scales = "free_y")
}
```

### By total number of recurrences
```{r plot joint by recurrences, fig.height = 7, fig.width = 12}
plot_joint1 <- plotJointProb(joint_summary, 1, 0, pal1) 
plot_joint2 <- plotJointProb(joint_summary, 2, 0, pal2) + guides(fill = guide_legend(ncol = 2)) 
plot_joint3 <- plotJointProb(joint_summary, 3, 0.001, pal3) + guides(fill = guide_legend(ncol = 3)) 
plot_joint4 <- plotJointProb(joint_summary, 4, 0.001, pal4) + guides(fill = guide_legend(ncol = 3)) 

(plot_joint1 | plot_joint2) / (plot_joint3 | plot_joint4) +
  plot_annotation(subtitle = "I = Reinfection, L = Relapse, C = Recrudescence",
                  caption = expression(italic("For >2 recurrences, only probability estimates > 0.001 are shown"))) +
  plot_layout(heights = c(2, 1))
```

### By treatment arm and number of recurrences
```{r plot joint by treatment arm, fig.height = 8, fig.width = 12}
joint_summary %>% 
    left_join(analysis_data %>% select(subject_id, treatment_arm) %>% distinct(),
              by = "subject_id") %>% 
    filter(joint_probability > 0.001) %>% 
    group_by(subject_id) %>% 
    mutate(state_pair = fct_reorder(state_pair, joint_probability)) %>%
    ungroup() %>% 
    ggplot(aes(x = joint_probability, 
               y = reorder(subject_id, total_recurrences), 
               fill = state_pair)) +
      geom_bar(position = "stack", stat = "identity") +
      scale_x_continuous(expand = c(0, 0)) +
      scale_y_discrete(expand = c(0, 0)) +
      scale_fill_manual(values = combined_pal) +
      labs(x = "Joint probability estimate",
           y = "Participant",
           fill = "Classification state",
           subtitle = "I = Reinfection, L = Relapse, C = Recrudescence",
           caption = expression(italic("Color coding: if > 1 recurrence, blue shades denote combinations of relapse and recrudescence (L and C), shades of orange/browns denote \ncombos of reinfection and relapse (I and L) and green shades denote combos of reinfection with recrudescence (I and C)"))) +
      theme_bw() + 
      facet_wrap(treatment_arm~total_recurrences, scales = "free") +
      # facet_wrap(total_recurrences~treatment_arm, scales = "free") +
      theme(plot.caption = element_text(hjust = 0, vjust = -2)) 
```

### 'Confidence' in highest joint probability estimate
What is the joint probability estimate for the 'highest ranking' state pair classification? I.e. if >80% relatively high confidence? n=19
```{r plot joint probability confidence}
joint_summary %>% 
  filter(joint_probability > 0.001) %>% 
  group_by(subject_id) %>% 
  # get the highest prob for each subj and store ranking
  mutate(ranking = rank(-joint_probability, ties.method = "first")) %>% 
  arrange(subject_id, ranking) %>% 
  filter(ranking == 1) %>% 
  ggplot(aes(x = reorder(subject_id, -joint_probability),
             y = joint_probability,
             fill = factor(total_recurrences))) +
    geom_col() +
    geom_hline(yintercept = 0.8, color = "darkgray", linetype = "dashed") +
    labs(x = "Participant",
         y = "Joint probability estimate for highest 'ranking' state pair",
         fill = "Number of recurrent episodes",
         caption = expression(italic("Dashed line indicates an arbitrary threshold of 80%"))) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

How many state pairs make up the arbitrary 80% threshold?
```{r plot joint prob for pair rankings, fig.height=6, fig.width=9}
joint_summary %>% 
  filter(joint_probability > 0.001) %>% 
  group_by(subject_id) %>% 
  # Get the highest prob for each subject and store ranking
  mutate(ranking = rank(-joint_probability, ties.method = "first")) %>% 
  # reverse ranking order of ranking so we plot the highest rank at the bottom
  arrange(subject_id, desc(ranking)) %>%
  mutate(ranking = factor(ranking, levels = unique(ranking))) %>% 
  ggplot(aes(x = subject_id,
             y = joint_probability,
             fill = ranking)) +
    geom_bar(stat = "identity", position = "stack") +
    geom_hline(yintercept = 0.8, color = "darkgray", linetype = "dashed") +
    scale_fill_manual(values = colorRampPalette(brewer.pal(12, "Paired"))(14),
                      guide = guide_legend(reverse = T)) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x = "Participant",
         y = "Joint probability estimate for state pair",
         fill = "Number of possible \nestimated state pairs",
         caption = expression(italic("Dashed line indicates an arbitrary threshold of 80%"))) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
    facet_wrap(~total_recurrences, scales = "free_x")
```

### Highest ranking classification by episode number
```{r create summary of top 3 rankings}
joint_top3_summary <- joint_summary %>% 
            select(-percentage) %>% 
            group_by(subject_id) %>% 
            # Get the highest prob for each subject and store ranking
            mutate(ranking = rank(-joint_probability, ties.method = "first")) %>% 
            arrange(subject_id, ranking) %>% 
            filter(ranking %in% c(1, 2, 3))
```

We will keep the 'highest ranking' joint probability estimate so we can compare the state classification for each episode with other metrics
```{r create summary of top ranking joint probability for each recurrence}
joint_recurrence_summary <- joint_top3_summary %>% 
  select(-total_recurrences) %>% 
  filter(ranking == 1) %>% 
  mutate(state_pair_split = strsplit(state_pair, "")) %>%  # Split state_pair into individual letters
  unnest(state_pair_split) %>%  # Unnest to create a new row for each letter
  group_by(subject_id) %>%
  mutate(
    episode_number = row_number() + 1,  # Create episode number starting from n+1 to reflect number of recurrence
    joint_probability = joint_probability,  # Keep the same joint_probability for all episodes\
    state_classification = case_when(state_pair_split == "L" ~ "Relapse",
                                     state_pair_split == "C" ~ "Recrudescence",
                                     state_pair_split == "I" ~ "Reinfection")) %>%
  select(subject_id, episode_number, state_classification, joint_probability)
```

```{r plot joint prob highest ranking by episode, fig.height=6, fig.width=8}
joint_recurrence_summary %>% 
  ggplot(aes(x = factor(episode_number), y = joint_probability, fill = state_classification)) +
    geom_col() +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Episode number", 
         y     = "Posterior joint probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~subject_id)
```

## Comparison marginal vs joint 
```{r create posterior summary}
posterior_summary <- marginal_summary %>% 
  rename("marginal_posterior_probability" = "posterior_value",
         "marginal_classification" = "posterior_classification") %>% 
  left_join(joint_recurrence_summary %>% 
               rename("joint_classification" = "state_classification",
                      "joint_posterior_probability" = "joint_probability"),
             by = c("subject_id", "episode_number")) %>% 
  pivot_longer(cols = c(marginal_posterior_probability, 
                        joint_posterior_probability, 
                        marginal_classification, 
                        joint_classification),
    names_to = c("estimate_type", ".value"),
    names_pattern = "(marginal|joint)_(posterior_probability|classification)") %>% 
  distinct() 
```

```{r plot marginal with joint probs, fig.height = 6, fig.width=8}
posterior_summary %>% 
  group_by(subject_id, episode_number) %>%
  arrange(desc(posterior_probability), .by_group = TRUE) %>%
  mutate(classification = factor(classification, levels = unique(classification))) %>%
  ungroup() %>% 
  ggplot(aes(x = factor(episode_number), y = posterior_probability, group = episode_number)) + 
    geom_bar(data = . %>% filter(estimate_type == "marginal"),
             aes(fill = classification),
             stat = "identity", 
             position = "fill") +
    geom_segment(data = . %>% filter(estimate_type == "joint"),
               aes(x = as.numeric(factor(episode_number)) - 0.4,  
                   xend = as.numeric(factor(episode_number)) + 0.4,  
                   y = posterior_probability,  
                   yend = posterior_probability, 
                   color = classification),
               size = 1.5) +
    geom_point(data = . %>% filter(estimate_type == "joint"),
               aes(color = classification,
                   fill = classification),
               shape = 21) +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_color_manual(values = c("Relapse" = "#006064",
                                 "Recrudescence" = "#3B4F75",
                                 "Reinfection" = "#8B008B")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Episode number", 
         y     = "Posterior probability", 
         fill = "Marginal classification",
         color = "Joint probability estimate \n and classification") + 
    theme_bw() +
    facet_wrap(~subject_id)
```

## Comparison to IBS and IBD estimates
```{r read pnh data}
sols_pnh <- read.csv(here("data/final", "sols_PNH_data.csv"))
ibd_r_data <- readRDS(here("data/final", "sols_all_meta.rds"))
ibd_rhat_data <- readRDS(here("data/final", "sols_sig_meta.rds"))
```

For direct comparison with the we can only look at the paired comparisons between baseline samples and follow-up samples, but not other pw comparisons for a given participant. 
```{r filter datasets}
sols_ibd <- ibd_r_data %>% 
              # filter to include only paired samples, and only pw comparisons to baseline
              filter(comparison_type == "paired", day_id2 == 0) %>% 
              mutate(subject_id = paste0("AR-", patientid1),
                     day_id1 = as.integer(day_id1),
                     day_id2 = as.integer(day_id2)) %>% 
              group_by(subject_id) %>%  
              arrange(day_id1) %>%  
              # create episode_number accounting for baseline as episode 1
              mutate(episode_number = row_number() + 1) %>% 
              ungroup()
```

```{r create classification summary}
classification_summary <- indiv_posteriors_marginal %>% 
  rename("posterior_probability_recrudescence" = "Posterior_marginal_prob_C",
         "posterior_probability_relapse" = "Posterior_marginal_prob_L",
         "posterior_probability_reinfection" = "Posterior_marginal_prob_I") %>% 
  # merge with epi variables
  left_join(analysis_data %>% select(subject_id, sample_id, episode_number, treatment_arm, visit_date, days_since_enrolment, days_since_last_episode) %>% distinct(), 
            by = c("subject_id", "episode_number")) %>% 
  # merge with PNH data
  left_join(sols_pnh, by = c("subject_id" = "Patient", "episode_number" = "recurrence")) %>% 
  rename("prop_same_haps_1minusPNH" = "Prop_het", 
         "pnh_range" = "identity", 
         "classification" = "Classification") %>% 
  # create bins for 1-PNH (IBS metric)
  mutate(ibs_range = cut(prop_same_haps_1minusPNH,  
                         breaks = c(0, 0.25, 0.5, 0.75, 1), 
                         include.lowest = TRUE, 
                         labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  # merge with IBD r
  left_join(sols_ibd,
            by = c("subject_id", "episode_number")) %>% 
  # create bins for IBD r
  mutate(ibd_range = cut(estimate,  
                         breaks = c(0, 0.25, 0.5, 0.75, 1), 
                         include.lowest = TRUE, 
                         labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  # merge with joint probabilities
  left_join(joint_recurrence_summary %>% 
              rename("joint_classification" = "state_classification"),
            by = c("subject_id", "episode_number"))

classification_summary
```

### Comparison with IBS
```{r classification comparison with IBS, eval=F}
classification_summary %>% 
  select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_range = cut(posterior_value, 
                               breaks = c(0, 0.25, 0.5, 0.75, 1), 
                               include.lowest = TRUE, 
                               labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  count(ibs_range, classification, posterior_type, posterior_range)
```

### Plot IBS range vs probabilistic classification (marginal probability)
```{r classification IBS comparison plot v marginal, fig.height=10, fig.width=8}
classification_summary %>% 
  select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_range = cut(posterior_value, 
                               breaks = c(0, 0.25, 0.5, 0.75, 1), 
                               include.lowest = TRUE, 
                               labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%  
  ggplot(aes(x = reorder(pair, episode_number), y = posterior_value, group = episode_number, fill = posterior_type)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_fill_manual(values = c("turquoise3", "magenta3", "skyblue4"),
                       labels = c("posterior_probability_relapse" = "Relapse",
                                  "posterior_probability_recrudescence" = "Recrudescence",
                                  "posterior_probability_reinfection" = "Reinfection")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Sample pair", 
         y     = "Posterior marginal probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~ibs_range, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```
### Plot IBS range vs probabilistic classification (joint probability, highest ranking)
```{r classification IBS comparison plot v joint, fig.height=10, fig.width=8}
classification_summary %>% 
  select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>% 
  ggplot(aes(x = reorder(pair, episode_number), y = joint_probability, group = episode_number, fill = joint_classification)) +
    geom_bar(stat = "identity") +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Sample pair", 
         y     = "Posterior joint probability (highest ranking classification)", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~ibs_range, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

### Comparison with IBD
```{r classification comparison with IBD, eval=F}
classification_summary %>% 
  select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), ibd_range, CI_lower, CI_upper, sig_est) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_range = cut(posterior_value, 
                               breaks = c(0, 0.25, 0.5, 0.75, 1), 
                               include.lowest = TRUE, 
                               labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  count(ibd_range, sig_est, posterior_type, posterior_range)
```

### For reference - all pairwise comparisons
```{r plot estimates and CI all pw comparisons, fig.height=16, fig.width=10}
ibd_r_data %>%  
  filter(comparison_type == "paired") %>% 
  ggplot(aes(x = pair, y = estimate, group = patientid1, color = sig_est)) +
    geom_point(size = 3) +
    geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
    scale_color_manual(values = c("not significant" = "darkgrey", 
                                  "significant" = "indianred3")) +
    facet_wrap(~patientid1, scales = "free_x") +
    theme_bw() +    
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

### Plot IBD estimate and CI for paired samples compared to baseline for each participant
```{r plot estimates and CI paired samples, fig.height=12, fig.width=8}
classification_summary %>% 
  ggplot(aes(x = reorder(sampleid1, days_since_enrolment), y = estimate, group = subject_id, color = sig_est)) +
    geom_point(size = 3) +
    geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
    scale_color_manual(values = c("not significant" = "darkgrey", 
                                  "significant" = "indianred3")) +
    labs(x = "Sample",
         y = "IBD",
         fill = "Significance") +
    facet_wrap(~subject_id, scales = "free_x") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

```{r plot estimates and CI not paired samples, eval=F}
ibd_r_data %>% 
  filter(comparison_type == "not paired") %>% 
  ggplot(aes(x = pair, y = estimate, group = patientid1, color = sig_est)) +
    geom_point(size = 3) +
    geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
    scale_color_manual(values = c("not significant" = "darkgrey", 
                                  "significant" = "indianred3")) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

### Plot IBD estimate and CI for paired samples compared to baseline for each participant, based on days since enrolment
```{r plot estimates and CI paired samples v days, fig.height=12, fig.width=8}
classification_summary %>% 
  ggplot(aes(x = days_since_enrolment, y = estimate, group = subject_id, color = sig_est)) +
    geom_point(size = 3) +
    geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
    scale_color_manual(values = c("not significant" = "darkgrey", 
                                  "significant" = "indianred3")) +
    labs(x = "Days since last episode",
         y = "IBD",
         fill = "Significance") +
    facet_wrap(~subject_id, scales = "free_x") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

### Plot IBD range vs probabilistic classification (marginal)
```{r classification IBD comparison plot, fig.height=10, fig.width=8}
classification_summary %>% 
  select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), ibd_range, CI_lower, CI_upper, sig_est) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_range = cut(posterior_value, 
                               breaks = c(0, 0.25, 0.5, 0.75, 1), 
                               include.lowest = TRUE, 
                               labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%  
  ggplot(aes(x = reorder(pair, episode_number), y = posterior_value, group = episode_number, fill = posterior_type)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_fill_manual(values = c("turquoise3", "magenta3", "skyblue4"),
                       labels = c("posterior_probability_relapse" = "Relapse",
                                  "posterior_probability_recrudescence" = "Recrudescence",
                                  "posterior_probability_reinfection" = "Reinfection")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Sample pair", 
         y     = "Posterior marginal probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~ibd_range, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

### Plot IBD range vs probabilistic classification (joint probability, highest ranking)
```{r classification IBD comparison plot v joint, fig.height=10, fig.width=8}
classification_summary %>% 
  select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("joint"), ibd_range, CI_lower, CI_upper, sig_est) %>% 
  ggplot(aes(x = reorder(pair, episode_number), y = joint_probability, group = episode_number, fill = joint_classification)) +
    geom_bar(stat = "identity") +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Sample pair", 
         y     = "Posterior joint probability (highest ranking classification)", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~ibd_range, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

## Confusion matrices: PNH vs probabilistic classification

### Classification by 1-PNH (IBS) range
```{r data for confusion matrix ibs range}
confusion_data_ibsrange <- classification_summary %>% 
  select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, 
         # renaming for clarity
         pnh_classification = classification) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "marginal_classification", 
               values_to = "marginal_probability") %>% 
  mutate(marginal_classification = case_when(marginal_classification == "posterior_probability_recrudescence" ~ "Recrudescence",
                                             marginal_classification == "posterior_probability_relapse" ~ "Relapse",
                                             marginal_classification == "posterior_probability_reinfection" ~ "Reinfection"),
         marginal_classification = factor(marginal_classification, 
                                          levels = c("Relapse", "Recrudescence", "Reinfection")),
         marginal_probability_range = cut(marginal_probability, 
                                          breaks = c(0, 0.25, 0.5, 0.75, 1), 
                                          include.lowest = TRUE, 
                                          labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
         joint_probability_range = cut(joint_probability, 
                                       breaks = c(0, 0.25, 0.5, 0.75, 1), 
                                       include.lowest = TRUE, 
                                       labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  select(subject_id, episode_number, days_since_enrolment, ibs_range, pnh_classification,  marginal_classification, marginal_probability, marginal_probability_range, joint_classification, joint_probability, joint_probability_range) %>% 
  # now only keep the highest 'ranking' marginal classification
  group_by(subject_id, episode_number) %>% 
  slice_max(order_by = marginal_probability, n = 1, with_ties = F) %>% 
  ungroup() %>% 
  group_by(ibs_range, marginal_classification, joint_classification) %>%
  tally() %>%
  dcast(ibs_range ~ marginal_classification + joint_classification, value.var = "n", fill = 0)
```

```{r plot confusion matrix ibs range}
confusion_data_ibsrange %>% 
  melt(id.vars = "ibs_range") %>% 
  ggplot(aes(ibs_range, variable, fill = value)) +
    geom_tile() +
    scale_fill_gradient(low = "white", high = "red") +
    geom_text(aes(label = value)) +
    theme_minimal() +
    labs(x = "IBS range (1-PNH)",
         y = "Marginal vs joint classification",
         fill = "Count")
```

### Classification comparison
```{r data for confusion matrix pnh state}
# Create a confusion matrix for each pair
confusion_data_pnhstate <- 
  classification_summary %>% 
  select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, 
         # renaming for clarity
         pnh_classification = classification) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "marginal_classification", 
               values_to = "marginal_probability") %>% 
  mutate(marginal_classification = case_when(marginal_classification == "posterior_probability_recrudescence" ~ "Recrudescence",
                                             marginal_classification == "posterior_probability_relapse" ~ "Relapse",
                                             marginal_classification == "posterior_probability_reinfection" ~ "Reinfection"),
         marginal_classification = factor(marginal_classification, 
                                          levels = c("Relapse", "Recrudescence", "Reinfection")),
         marginal_probability_range = cut(marginal_probability, 
                                          breaks = c(0, 0.25, 0.5, 0.75, 1), 
                                          include.lowest = TRUE, 
                                          labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
         joint_probability_range = cut(joint_probability, 
                                       breaks = c(0, 0.25, 0.5, 0.75, 1), 
                                       include.lowest = TRUE, 
                                       labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
         # pnh_state = case_when(pnh_classification == "Heterologous" ~ "Reinfection",
         #                       pnh_classification == "Homologous" ~ "Relapse or Recrudescence")
         pnh_state = case_when(ibs_range == "0-0.25" | ibs_range == "0.25-0.5" ~ "Reinfection",
                               (ibs_range == "0.5-0.75" | ibs_range == "0.75-1") ~ "Relapse or Recrudescence")
         ) %>% 
  select(subject_id, episode_number, days_since_enrolment, ibs_range, pnh_classification, pnh_state, marginal_classification, marginal_probability, marginal_probability_range, joint_classification, joint_probability, joint_probability_range) %>% 
  # now only keep the highest 'ranking' marginal classification
  group_by(subject_id, episode_number) %>% 
  slice_max(order_by = marginal_probability, n = 1, with_ties = F) %>% 
  ungroup() %>% 
  group_by(pnh_state, marginal_classification, joint_classification) %>%
  tally() %>%
  dcast(pnh_state ~ marginal_classification + joint_classification, value.var = "n", fill = 0)
```

```{r plot confusion matrix pnh state}
confusion_data_pnhstate %>% 
  melt(id.vars = "pnh_state") %>% 
  ggplot(aes(pnh_state, variable, fill = value)) +
    geom_tile() +
    scale_fill_gradient(low = "white", high = "red") +
    geom_text(aes(label = value)) +
    theme_minimal() +
    labs(x = "PNH classification",
         y = "Marginal vs joint classification",
         fill = "Count")
```